Amazon MLS-C01 Practice Exams
Last updated on Oct 22,2026- Exam Code: MLS-C01
- Exam Name: AWS Certified Machine Learning - Specialty
- Certification Provider: Amazon
- Latest update: Oct 22,2026
Question #31
A company offers an online shopping service to its customers. The company wants to enhance the site’s security by requesting additional information when customers access the site from locations that are different from their normal location. The company wants to update the process to call a machine learning (ML) model to determine when additional information should be requested. The company has several terabytes of data from its existing ecommerce web servers containing the source IP addresses for each request made to the web server. For authenticated requests, the records also contain the login name of the requesting user.
Which approach should an ML specialist take to implement the new security feature in the web application?
- A . Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the factorization machines (FM) algorithm.
- B . Use Amazon SageMaker to train a model using the IP Insights algorithm. Schedule updates and retraining of the model using new log data nightly.
- C . Use Amazon SageMaker Ground Truth to label each record as either a successful or failed access attempt. Use Amazon SageMaker to train a binary classification model using the IP Insights algorithm.
- D . Use Amazon SageMaker to train a model using the Object2Vec algorithm. Schedule updates and retraining of the model using new log data nightly.
Correct Answer: B
B
Explanation:
The IP Insights algorithm is designed to capture associations between entities and IP addresses, and can be used to identify anomalous IP usage patterns. The algorithm can learn from historical data that contains pairs of entities and IP addresses, and can return a score that indicates how likely the pair is to occur. The company can use this algorithm to train a model that can detect when a customer is accessing the site from a different location than usual, and request additional information accordingly. The company can also schedule updates and retraining of the model using new log data nightly to keep the model up to date with the latest IP usage patterns. The other options are not suitable for this use case because:
Option A: The factorization machines (FM) algorithm is a general-purpose supervised learning algorithm that can be used for both classification and regression tasks. However, it is not optimized for capturing associations between entities and IP addresses, and would require labeling each record as either a successful or failed access attempt, which is a costly and time-consuming process. Option C: The IP Insights algorithm is a good choice for this use case, but it does not require labeling each record as either a successful or failed access attempt. The algorithm is unsupervised and can learn from the historical data without labels. Labeling the data would be unnecessary and wasteful. Option D: The Object2Vec algorithm is a general-purpose neural embedding algorithm that can learn low-dimensional dense embeddings of high-dimensional objects. However, it is not designed to capture associations between entities and IP addresses, and would require a different input format than the one provided by the company. The Object2Vec algorithm expects pairs of objects and their relationship labels or scores as inputs, while the company has data containing the source IP addresses and the login names of the requesting users.
References:
IP Insights – Amazon SageMaker
Factorization Machines Algorithm – Amazon SageMaker
Object2Vec Algorithm – Amazon SageMaker
B
Explanation:
The IP Insights algorithm is designed to capture associations between entities and IP addresses, and can be used to identify anomalous IP usage patterns. The algorithm can learn from historical data that contains pairs of entities and IP addresses, and can return a score that indicates how likely the pair is to occur. The company can use this algorithm to train a model that can detect when a customer is accessing the site from a different location than usual, and request additional information accordingly. The company can also schedule updates and retraining of the model using new log data nightly to keep the model up to date with the latest IP usage patterns. The other options are not suitable for this use case because:
Option A: The factorization machines (FM) algorithm is a general-purpose supervised learning algorithm that can be used for both classification and regression tasks. However, it is not optimized for capturing associations between entities and IP addresses, and would require labeling each record as either a successful or failed access attempt, which is a costly and time-consuming process. Option C: The IP Insights algorithm is a good choice for this use case, but it does not require labeling each record as either a successful or failed access attempt. The algorithm is unsupervised and can learn from the historical data without labels. Labeling the data would be unnecessary and wasteful. Option D: The Object2Vec algorithm is a general-purpose neural embedding algorithm that can learn low-dimensional dense embeddings of high-dimensional objects. However, it is not designed to capture associations between entities and IP addresses, and would require a different input format than the one provided by the company. The Object2Vec algorithm expects pairs of objects and their relationship labels or scores as inputs, while the company has data containing the source IP addresses and the login names of the requesting users.
References:
IP Insights – Amazon SageMaker
Factorization Machines Algorithm – Amazon SageMaker
Object2Vec Algorithm – Amazon SageMaker
Question #32
A telecommunications company is developing a mobile app for its customers. The company is using an Amazon SageMaker hosted endpoint for machine learning model inferences.
Developers want to introduce a new version of the model for a limited number of users who subscribed to a preview feature of the app. After the new version of the model is tested as a preview, developers will evaluate its accuracy. If a new version of the model has better accuracy, developers need to be able to gradually release the new version for all users over a fixed period of time.
How can the company implement the testing model with the LEAST amount of operational overhead?
- A . Update the ProductionVariant data type with the new version of the model by using the CreateEndpointConfig operation with the InitialVariantWeight parameter set to 0. Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview feature. When the new version of the model is ready for release, gradually increase InitialVariantWeight until all users have the updated version.
- B . Configure two SageMaker hosted endpoints that serve the different versions of the model. Create an Application Load Balancer (ALB) to route traffic to both endpoints based on the TargetVariant query string parameter. Reconfigure the app to send the TargetVariant query string parameter for users who subscribed to the preview feature. When the new version of the model is ready for release, change the ALB’s routing algorithm to weighted until all users have the updated version.
- C . Update the DesiredWeightsAndCapacity data type with the new version of the model by using the UpdateEndpointWeightsAndCapacities operation with the DesiredWeight parameter set to 0. Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview feature. When the new version of the model is ready for release, gradually increase DesiredWeight until all users have the updated version.
- D . Configure two SageMaker hosted endpoints that serve the different versions of the model. Create an Amazon Route 53 record that is configured with a simple routing policy and that points to the current version of the model. Configure the mobile app to use the endpoint URL for users who subscribed to the preview feature and to use the Route 53 record for other users. When the new version of the model is ready for release, add a new model version endpoint to Route 53, and switch the policy to weighted until all users have the updated version.
Correct Answer: C
C
Explanation:
The best solution for implementing the testing model with the least amount of operational overhead is to use the following steps:
Update the DesiredWeightsAndCapacity data type with the new version of the model by using the UpdateEndpointWeightsAndCapacities operation with the DesiredWeight parameter set to 0. This operation allows the developers to update the variant weights and capacities of an existing SageMaker endpoint without deleting and recreating the endpoint. Setting the DesiredWeight parameter to 0 means that the new version of the model will not receive any traffic initially1
Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview feature. This parameter allows the developers to override the variant weights and direct a request to a specific variant. This way, the developers can test the new version of the model for a limited number of users who opted in for the preview feature2
When the new version of the model is ready for release, gradually increase DesiredWeight until all users have the updated version. This operation allows the developers to perform a gradual rollout of the new version of the model and monitor its performance and accuracy. The developers can adjust the variant weights and capacities as needed until the new version of the model serves all the traffic1
The other options are incorrect because they either require more operational overhead or do not support the desired use cases.
For example:
Option A uses the CreateEndpointConfig operation with the InitialVariantWeight parameter set to 0. This operation creates a new endpoint configuration, which requires deleting and recreating the endpoint to apply the changes. This adds extra overhead and downtime for the endpoint. It also does not support the gradual rollout of the new version of the model3
Option B uses two SageMaker hosted endpoints that serve the different versions of the model and an Application Load Balancer (ALB) to route traffic to both endpoints based on the TargetVariant query string parameter. This option requires creating and managing additional resources and services, such as the second endpoint and the ALB. It also requires changing the app code to send the query string parameter for the preview feature4
Option D uses the access key and secret key of the IAM user with appropriate KMS and ECR permissions. This is not a secure way to pass credentials to the Processing job.
It also requires the ML specialist to manage the IAM user and the keys.
Reference:
1: UpdateEndpointWeightsAndCapacities – Amazon SageMaker
2: InvokeEndpoint – Amazon SageMaker
3: CreateEndpointConfig – Amazon SageMaker
4: Application Load Balancer – Elastic Load Balancing
C
Explanation:
The best solution for implementing the testing model with the least amount of operational overhead is to use the following steps:
Update the DesiredWeightsAndCapacity data type with the new version of the model by using the UpdateEndpointWeightsAndCapacities operation with the DesiredWeight parameter set to 0. This operation allows the developers to update the variant weights and capacities of an existing SageMaker endpoint without deleting and recreating the endpoint. Setting the DesiredWeight parameter to 0 means that the new version of the model will not receive any traffic initially1
Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview feature. This parameter allows the developers to override the variant weights and direct a request to a specific variant. This way, the developers can test the new version of the model for a limited number of users who opted in for the preview feature2
When the new version of the model is ready for release, gradually increase DesiredWeight until all users have the updated version. This operation allows the developers to perform a gradual rollout of the new version of the model and monitor its performance and accuracy. The developers can adjust the variant weights and capacities as needed until the new version of the model serves all the traffic1
The other options are incorrect because they either require more operational overhead or do not support the desired use cases.
For example:
Option A uses the CreateEndpointConfig operation with the InitialVariantWeight parameter set to 0. This operation creates a new endpoint configuration, which requires deleting and recreating the endpoint to apply the changes. This adds extra overhead and downtime for the endpoint. It also does not support the gradual rollout of the new version of the model3
Option B uses two SageMaker hosted endpoints that serve the different versions of the model and an Application Load Balancer (ALB) to route traffic to both endpoints based on the TargetVariant query string parameter. This option requires creating and managing additional resources and services, such as the second endpoint and the ALB. It also requires changing the app code to send the query string parameter for the preview feature4
Option D uses the access key and secret key of the IAM user with appropriate KMS and ECR permissions. This is not a secure way to pass credentials to the Processing job.
It also requires the ML specialist to manage the IAM user and the keys.
Reference:
1: UpdateEndpointWeightsAndCapacities – Amazon SageMaker
2: InvokeEndpoint – Amazon SageMaker
3: CreateEndpointConfig – Amazon SageMaker
4: Application Load Balancer – Elastic Load Balancing
Question #33
A finance company has collected stock return data for 5.000 publicly traded companies. A financial analyst has a dataset that contains 2.000 attributes for each company. The financial analyst wants to use Amazon SageMaker to identify the top 15 attributes that are most valuable to predict future stock returns.
Which solution will meet these requirements with the LEAST operational overhead?
- A . Use the linear learner algorithm in SageMaker to train a linear regression model to predict the stock returns. Identify the most predictive features by ranking absolute coefficient values.
- B . Use random forest regression in SageMaker to train a model to predict the stock returns. Identify the most predictive features based on Gini importance scores.
- C . Use an Amazon SageMaker Data Wrangler quick model visualization to predict the stock returns.
Identify the most predictive features based on the quick model’s feature importance scores. - D . Use Amazon SageMaker Autopilot to build a regression model to predict the stock returns. Identify the most predictive features based on an Amazon SageMaker Clarify report.
Correct Answer: D
D
Explanation:
Amazon SageMaker Autopilot is a fully managed solution that automatically explores different ML models and selects the most effective ones for a given prediction task. After model training, Amazon SageMaker Clarify can generate feature importance scores, identifying the top features in a straightforward, automated manner with minimal manual intervention.
By using SageMaker Autopilot, the data scientist can obtain the desired feature importance ranking for predictive attributes with minimal setup and low operational overhead, as opposed to manually configuring models in SageMaker.
D
Explanation:
Amazon SageMaker Autopilot is a fully managed solution that automatically explores different ML models and selects the most effective ones for a given prediction task. After model training, Amazon SageMaker Clarify can generate feature importance scores, identifying the top features in a straightforward, automated manner with minimal manual intervention.
By using SageMaker Autopilot, the data scientist can obtain the desired feature importance ranking for predictive attributes with minimal setup and low operational overhead, as opposed to manually configuring models in SageMaker.
Question #34
A company has raw user and transaction data stored in AmazonS3 a MySQL database, and Amazon RedShift A Data Scientist needs to perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon RedShift, and then calculating the average-of a few selected columns from the joined data
Which AWS service should the Data Scientist use?
- A . Amazon Athena
- B . Amazon Redshift Spectrum
- C . AWS Glue
- D . Amazon QuickSight
Correct Answer: A
A
Explanation:
Amazon Athena is a serverless interactive query service that can analyze data in Amazon S3 using standard SQL. Amazon Athena can also query data from other sources, such as MySQL and Amazon Redshift, by using federated queries. Federated queries allow Amazon Athena to run SQL queries across data sources, such as relational and non-relational databases, data warehouses, and data lakes. By using Amazon Athena, the Data Scientist can perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon Redshift, and then calculating the average of a few selected columns from the joined data. Amazon Athena can also integrate with other AWS services, such as AWS Glue and Amazon QuickSight, to provide additional features, such as data cataloging and visualization.
References:
What is Amazon Athena? – Amazon Athena
Federated Query Overview – Amazon Athena
Querying Data from Amazon S3 – Amazon Athena
Querying Data from MySQL – Amazon Athena
[Querying Data from Amazon Redshift – Amazon Athena]
A
Explanation:
Amazon Athena is a serverless interactive query service that can analyze data in Amazon S3 using standard SQL. Amazon Athena can also query data from other sources, such as MySQL and Amazon Redshift, by using federated queries. Federated queries allow Amazon Athena to run SQL queries across data sources, such as relational and non-relational databases, data warehouses, and data lakes. By using Amazon Athena, the Data Scientist can perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon Redshift, and then calculating the average of a few selected columns from the joined data. Amazon Athena can also integrate with other AWS services, such as AWS Glue and Amazon QuickSight, to provide additional features, such as data cataloging and visualization.
References:
What is Amazon Athena? – Amazon Athena
Federated Query Overview – Amazon Athena
Querying Data from Amazon S3 – Amazon Athena
Querying Data from MySQL – Amazon Athena
[Querying Data from Amazon Redshift – Amazon Athena]
Question #35
A data scientist has a dataset of machine part images stored in Amazon Elastic File System (Amazon EFS). The data scientist needs to use Amazon SageMaker to create and train an image classification machine learning model based on this dataset. Because of budget and time constraints, management wants the data scientist to create and train a model with the least number of steps and integration work required.
How should the data scientist meet these requirements?
- A . Mount the EFS file system to a SageMaker notebook and run a script that copies the data to an Amazon FSx for Lustre file system. Run the SageMaker training job with the FSx for Lustre file system as the data source.
- B . Launch a transient Amazon EMR cluster. Configure steps to mount the EFS file system and copy the data to an Amazon S3 bucket by using S3DistCp. Run the SageMaker training job with Amazon S3 as the data source.
- C . Mount the EFS file system to an Amazon EC2 instance and use the AWS CLI to copy the data to an Amazon S3 bucket. Run the SageMaker training job with Amazon S3 as the data source.
- D . Run a SageMaker training job with an EFS file system as the data source.
Correct Answer: D
D
Explanation:
The simplest and fastest way to use the EFS dataset for SageMaker training is to run a SageMaker training job with an EFS file system as the data source. This option does not require any data copying or additional integration steps. SageMaker supports EFS as a data source for training jobs, and it can mount the EFS file system to the training container using the FileSystemConfig parameter. This way, the training script can access the data files as if they were on the local disk of the training instance.
References:
Access Training Data – Amazon SageMaker
Mount an EFS file system to an Amazon SageMaker notebook (with lifecycle configurations) | AWS Machine Learning Blog
D
Explanation:
The simplest and fastest way to use the EFS dataset for SageMaker training is to run a SageMaker training job with an EFS file system as the data source. This option does not require any data copying or additional integration steps. SageMaker supports EFS as a data source for training jobs, and it can mount the EFS file system to the training container using the FileSystemConfig parameter. This way, the training script can access the data files as if they were on the local disk of the training instance.
References:
Access Training Data – Amazon SageMaker
Mount an EFS file system to an Amazon SageMaker notebook (with lifecycle configurations) | AWS Machine Learning Blog
Question #36
A data scientist receives a collection of insurance claim records. Each record includes a claim ID. the final outcome of the insurance claim, and the date of the final outcome.
The final outcome of each claim is a selection from among 200 outcome categories. Some claim records include only partial information. However, incomplete claim records include only 3 or 4 outcome …gones from among the 200 available outcome categories. The collection includes hundreds of records for each outcome category. The records are from the previous 3 years.
The data scientist must create a solution to predict the number of claims that will be in each outcome category every month, several months in advance.
Which solution will meet these requirements?
- A . Perform classification every month by using supervised learning of the 20X3 outcome categories based on claim contents.
- B . Perform reinforcement learning by using claim IDs and dates Instruct the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month
- C . Perform forecasting by using claim IDs and dates to identify the expected number of claims in each outcome category every month.
- D . Perform classification by using supervised learning of the outcome categories for which partial information on claim contents is provided. Perform forecasting by using claim IDs and dates for all other outcome categories.
Correct Answer: C
C
Explanation:
The best solution for this scenario is to perform forecasting by using claim IDs and dates to identify the expected number of claims in each outcome category every month.
This solution has the following advantages:
It leverages the historical data of claim outcomes and dates to capture the temporal patterns and trends of the claims in each category1.
It does not require the claim contents or any other features to make predictions, which simplifies the data preparation and reduces the impact of missing or incomplete data2.
It can handle the high cardinality of the outcome categories, as forecasting models can output multiple values for each time point3.
It can provide predictions for several months in advance, which is useful for planning and budgeting purposes4.
The other solutions have the following drawbacks:
A: Performing classification every month by using supervised learning of the 200 outcome categories based on claim contents is not suitable, because it assumes that the claim contents are available and complete for all the records, which is not the case in this scenario2. Moreover, classification models usually output a single label for each input, which is not adequate for predicting the number of claims in each category3. Additionally, classification models do not account for the temporal aspect of the data, which is important for forecasting1.
B: Performing reinforcement learning by using claim IDs and dates and instructing the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month is not feasible, because it requires a feedback loop between the model and the agents, which might not be available or reliable in this scenario5. Furthermore, reinforcement learning is more suitable for sequential decision making problems, where the model learns from its actions and rewards, rather than forecasting problems, where the model learns from historical data and outputs future values6.
D: Performing classification by using supervised learning of the outcome categories for which partial information on claim contents is provided and performing forecasting by using claim IDs and dates for all other outcome categories is not optimal, because it combines two different methods that might not be consistent or compatible with each other7. Also, this solution suffers from the same limitations as solution A, such as the dependency on claim contents, the inability to handle multiple outputs, and the ignorance of temporal patterns123.
References:
1: Time Series Forecasting – Amazon SageMaker
2: Handling Missing Data for Machine Learning | AWS Machine Learning Blog
3: Forecasting vs Classification: What’s the Difference? | DataRobot
4: Amazon Forecast C Time Series Forecasting Made Easy | AWS News Blog
5: Reinforcement Learning – Amazon SageMaker
6: What is Reinforcement Learning?. The Complete Guide | Edureka
7: Combining Machine Learning Models | by Will Koehrsen | Towards Data Science
C
Explanation:
The best solution for this scenario is to perform forecasting by using claim IDs and dates to identify the expected number of claims in each outcome category every month.
This solution has the following advantages:
It leverages the historical data of claim outcomes and dates to capture the temporal patterns and trends of the claims in each category1.
It does not require the claim contents or any other features to make predictions, which simplifies the data preparation and reduces the impact of missing or incomplete data2.
It can handle the high cardinality of the outcome categories, as forecasting models can output multiple values for each time point3.
It can provide predictions for several months in advance, which is useful for planning and budgeting purposes4.
The other solutions have the following drawbacks:
A: Performing classification every month by using supervised learning of the 200 outcome categories based on claim contents is not suitable, because it assumes that the claim contents are available and complete for all the records, which is not the case in this scenario2. Moreover, classification models usually output a single label for each input, which is not adequate for predicting the number of claims in each category3. Additionally, classification models do not account for the temporal aspect of the data, which is important for forecasting1.
B: Performing reinforcement learning by using claim IDs and dates and instructing the insurance agents who submit the claim records to estimate the expected number of claims in each outcome category every month is not feasible, because it requires a feedback loop between the model and the agents, which might not be available or reliable in this scenario5. Furthermore, reinforcement learning is more suitable for sequential decision making problems, where the model learns from its actions and rewards, rather than forecasting problems, where the model learns from historical data and outputs future values6.
D: Performing classification by using supervised learning of the outcome categories for which partial information on claim contents is provided and performing forecasting by using claim IDs and dates for all other outcome categories is not optimal, because it combines two different methods that might not be consistent or compatible with each other7. Also, this solution suffers from the same limitations as solution A, such as the dependency on claim contents, the inability to handle multiple outputs, and the ignorance of temporal patterns123.
References:
1: Time Series Forecasting – Amazon SageMaker
2: Handling Missing Data for Machine Learning | AWS Machine Learning Blog
3: Forecasting vs Classification: What’s the Difference? | DataRobot
4: Amazon Forecast C Time Series Forecasting Made Easy | AWS News Blog
5: Reinforcement Learning – Amazon SageMaker
6: What is Reinforcement Learning?. The Complete Guide | Edureka
7: Combining Machine Learning Models | by Will Koehrsen | Towards Data Science
Question #37
A company wants to enhance audits for its machine learning (ML) systems. The auditing system must be able to perform metadata analysis on the features that the ML models use. The audit solution must generate a report that analyzes the metadata. The solution also must be able to set the data sensitivity and authorship of features.
Which solution will meet these requirements with the LEAST development effort?
- A . Use Amazon SageMaker Feature Store to select the features. Create a data flow to perform feature-level metadata analysis. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.
- B . Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use SageMaker Studio to analyze the
metadata. - C . Use Amazon SageMaker Features Store to apply custom algorithms to analyze the feature-level metadata that the company requires. Create an Amazon DynamoDB table to store feature-level metadata. Use Amazon QuickSight to analyze the metadata.
- D . Use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use. Assign the required metadata for each feature. Use Amazon QuickSight to analyze the metadata.
Correct Answer: D
D
Explanation:
The solution that will meet the requirements with the least development effort is to use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use, assign the required metadata for each feature, and use Amazon QuickSight to analyze the metadata. This solution can leverage the existing AWS services and features to perform feature-level metadata analysis and reporting.
Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, search, and share machine learning (ML) features. The service provides feature management capabilities such as enabling easy feature reuse, low latency serving, time travel, and ensuring consistency between features used in training and inference workflows. A feature group is a logical grouping of ML features whose organization and structure is defined by a feature group schema. A feature group schema consists of a list of feature definitions, each of which specifies the name, type, and metadata of a feature. The metadata can include information such as data sensitivity, authorship, description, and parameters. The metadata can help make features discoverable, understandable, and traceable. Amazon SageMaker Feature Store allows users to set feature groups for the current features that the ML models use, and assign the required metadata for each feature using the AWS SDK for Python (Boto3), AWS Command Line Interface (AWS CLI), or Amazon SageMaker Studio1.
Amazon QuickSight is a fully managed, serverless business intelligence service that makes it easy to create and publish interactive dashboards that include ML insights. Amazon QuickSight can connect to various data sources, such as Amazon S3, Amazon Athena, Amazon Redshift, and Amazon SageMaker Feature Store, and analyze the data using standard SQL or built-in ML-powered analytics. Amazon QuickSight can also create rich visualizations and reports that can be accessed from any device, and securely shared with anyone inside or outside an organization. Amazon QuickSight can be used to analyze the metadata of the features stored in Amazon SageMaker Feature Store, and generate a report that summarizes the metadata analysis2.
The other options are either more complex or less effective than the proposed solution. Using Amazon SageMaker Data Wrangler to select the features and create a data flow to perform feature-level metadata analysis would require additional steps and resources, and may not capture all the metadata attributes that the company requires. Creating an Amazon DynamoDB table to store feature-level metadata would introduce redundancy and inconsistency, as the metadata is already stored in Amazon SageMaker Feature Store. Using SageMaker Studio to analyze the metadata would not generate a report that can be easily shared and accessed by the company.
References:
1: Amazon SageMaker Feature Store C Amazon Web Services
2: Amazon QuickSight C Business Intelligence Service – Amazon Web Services
D
Explanation:
The solution that will meet the requirements with the least development effort is to use Amazon SageMaker Feature Store to set feature groups for the current features that the ML models use, assign the required metadata for each feature, and use Amazon QuickSight to analyze the metadata. This solution can leverage the existing AWS services and features to perform feature-level metadata analysis and reporting.
Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, search, and share machine learning (ML) features. The service provides feature management capabilities such as enabling easy feature reuse, low latency serving, time travel, and ensuring consistency between features used in training and inference workflows. A feature group is a logical grouping of ML features whose organization and structure is defined by a feature group schema. A feature group schema consists of a list of feature definitions, each of which specifies the name, type, and metadata of a feature. The metadata can include information such as data sensitivity, authorship, description, and parameters. The metadata can help make features discoverable, understandable, and traceable. Amazon SageMaker Feature Store allows users to set feature groups for the current features that the ML models use, and assign the required metadata for each feature using the AWS SDK for Python (Boto3), AWS Command Line Interface (AWS CLI), or Amazon SageMaker Studio1.
Amazon QuickSight is a fully managed, serverless business intelligence service that makes it easy to create and publish interactive dashboards that include ML insights. Amazon QuickSight can connect to various data sources, such as Amazon S3, Amazon Athena, Amazon Redshift, and Amazon SageMaker Feature Store, and analyze the data using standard SQL or built-in ML-powered analytics. Amazon QuickSight can also create rich visualizations and reports that can be accessed from any device, and securely shared with anyone inside or outside an organization. Amazon QuickSight can be used to analyze the metadata of the features stored in Amazon SageMaker Feature Store, and generate a report that summarizes the metadata analysis2.
The other options are either more complex or less effective than the proposed solution. Using Amazon SageMaker Data Wrangler to select the features and create a data flow to perform feature-level metadata analysis would require additional steps and resources, and may not capture all the metadata attributes that the company requires. Creating an Amazon DynamoDB table to store feature-level metadata would introduce redundancy and inconsistency, as the metadata is already stored in Amazon SageMaker Feature Store. Using SageMaker Studio to analyze the metadata would not generate a report that can be easily shared and accessed by the company.
References:
1: Amazon SageMaker Feature Store C Amazon Web Services
2: Amazon QuickSight C Business Intelligence Service – Amazon Web Services
Question #38
A company ingests machine learning (ML) data from web advertising clicks into an Amazon S3 data lake. Click data is added to an Amazon Kinesis data stream by using the Kinesis Producer Library (KPL). The data is loaded into the S3 data lake from the data stream by using an Amazon Kinesis Data Firehose delivery stream. As the data volume increases, an ML specialist notices that the rate of data ingested into Amazon S3 is relatively constant. There also is an increasing backlog of data for Kinesis Data Streams and Kinesis Data Firehose to ingest.
Which next step is MOST likely to improve the data ingestion rate into Amazon S3?
- A . Increase the number of S3 prefixes for the delivery stream to write to.
- B . Decrease the retention period for the data stream.
- C . Increase the number of shards for the data stream.
- D . Add more consumers using the Kinesis Client Library (KCL).
Correct Answer: C
C
Explanation:
The solution C is the most likely to improve the data ingestion rate into Amazon S3 because it increases the number of shards for the data stream. The number of shards determines the throughput capacity of the data stream, which affects the rate of data ingestion. Each shard can support up to 1 MB per second of data input and 2 MB per second of data output. By increasing the number of shards, the company can increase the data ingestion rate proportionally. The company can use the UpdateShardCount API operation to modify the number of shards in the data stream1.
The other options are not likely to improve the data ingestion rate into Amazon S3 because:
Option A: Increasing the number of S3 prefixes for the delivery stream to write to will not affect the data ingestion rate, as it only changes the way the data is organized in the S3 bucket. The number of S3 prefixes can help to optimize the performance of downstream applications that read the data from S3, but it does not impact the performance of Kinesis Data Firehose2.
Option B: Decreasing the retention period for the data stream will not affect the data ingestion rate, as it only changes the amount of time the data is stored in the data stream. The retention period can help to manage the data availability and durability, but it does not impact the throughput capacity of the data stream3.
Option D: Adding more consumers using the Kinesis Client Library (KCL) will not affect the data ingestion rate, as it only changes the way the data is processed by downstream applications. The consumers can help to scale the data processing and handle failures, but they do not impact the data ingestion into S3 by Kinesis Data Firehose4.
References:
1: Resharding – Amazon Kinesis Data Streams
2: Amazon S3 Prefixes – Amazon Kinesis Data Firehose
3: Data Retention – Amazon Kinesis Data Streams
4: Developing Consumers Using the Kinesis Client Library – Amazon Kinesis Data Streams
C
Explanation:
The solution C is the most likely to improve the data ingestion rate into Amazon S3 because it increases the number of shards for the data stream. The number of shards determines the throughput capacity of the data stream, which affects the rate of data ingestion. Each shard can support up to 1 MB per second of data input and 2 MB per second of data output. By increasing the number of shards, the company can increase the data ingestion rate proportionally. The company can use the UpdateShardCount API operation to modify the number of shards in the data stream1.
The other options are not likely to improve the data ingestion rate into Amazon S3 because:
Option A: Increasing the number of S3 prefixes for the delivery stream to write to will not affect the data ingestion rate, as it only changes the way the data is organized in the S3 bucket. The number of S3 prefixes can help to optimize the performance of downstream applications that read the data from S3, but it does not impact the performance of Kinesis Data Firehose2.
Option B: Decreasing the retention period for the data stream will not affect the data ingestion rate, as it only changes the amount of time the data is stored in the data stream. The retention period can help to manage the data availability and durability, but it does not impact the throughput capacity of the data stream3.
Option D: Adding more consumers using the Kinesis Client Library (KCL) will not affect the data ingestion rate, as it only changes the way the data is processed by downstream applications. The consumers can help to scale the data processing and handle failures, but they do not impact the data ingestion into S3 by Kinesis Data Firehose4.
References:
1: Resharding – Amazon Kinesis Data Streams
2: Amazon S3 Prefixes – Amazon Kinesis Data Firehose
3: Data Retention – Amazon Kinesis Data Streams
4: Developing Consumers Using the Kinesis Client Library – Amazon Kinesis Data Streams
Question #39
A Data Scientist needs to migrate an existing on-premises ETL process to the cloud. The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing
The Data Scientist has been given the following requirements for the cloud solution
* Combine multiple data sources
* Reuse existing PySpark logic
* Run the solution on the existing schedule
* Minimize the number of servers that will need to be managed.
Which architecture should the Data Scientist use to build this solution?
- A . Write the raw data to Amazon S3 Schedule an AWS Lambda function to submit a Spark step to a persistent Amazon EMR cluster based on the existing schedule Use the existing PySpark logic to run the ETL job on the EMR cluster Output the results to a "processed" location m Amazon S3 that is accessible tor downstream use
- B . Write the raw data to Amazon S3 Create an AWS Glue ETL job to perform the ETL processing against the input data Write the ETL job in PySpark to leverage the existing logic Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule Configure the output target of the ETL job to write to a "processed" location in Amazon S3 that is accessible for downstream use.
- C . Write the raw data to Amazon S3 Schedule an AWS Lambda function to run on the existing schedule and process the input data from Amazon S3 Write the Lambda logic in Python and implement the existing PySpartc logic to perform the ETL process Have the Lambda function output the results to a "processed" location in Amazon S3 that is accessible for downstream use
- D . Use Amazon Kinesis Data Analytics to stream the input data and perform realtime SQL queries against the stream to carry out the required transformations within the stream Deliver the output results to a "processed" location in Amazon S3 that is accessible for downstream use
Correct Answer: B
B
Explanation:
The Data Scientist needs to migrate an existing on-premises ETL process to the cloud, using a solution that can combine multiple data sources, reuse existing PySpark logic, run on the existing schedule, and minimize the number of servers that need to be managed. The best architecture for this scenario is to use AWS Glue, which is a serverless data integration service that can create and run ETL jobs on AWS.
AWS Glue can perform the following tasks to meet the requirements:
Combine multiple data sources: AWS Glue can access data from various sources, such as Amazon S3, Amazon RDS, Amazon Redshift, Amazon DynamoDB, and more. AWS Glue can also crawl the data sources and discover their schemas, formats, and partitions, and store them in the AWS Glue Data Catalog, which is a centralized metadata repository for all the data assets.
Reuse existing PySpark logic: AWS Glue supports writing ETL scripts in Python or Scala, using Apache Spark as the underlying execution engine. AWS Glue provides a library of built-in transformations and connectors that can simplify the ETL code. The Data Scientist can write the ETL job in PySpark and leverage the existing logic to perform the data processing.
Run the solution on the existing schedule: AWS Glue can create triggers that can start ETL jobs based on a schedule, an event, or a condition. The Data Scientist can create a new AWS Glue trigger to run the ETL job based on the existing schedule, using a cron expression or a relative time interval.
Minimize the number of servers that need to be managed: AWS Glue is a serverless service, which means that it automatically provisions, configures, scales, and manages the compute resources
required to run the ETL jobs. The Data Scientist does not need to worry about setting up, maintaining, or monitoring any servers or clusters for the ETL process.
Therefore, the Data Scientist should use the following architecture to build the cloud solution: Write the raw data to Amazon S3: The Data Scientist can use any method to upload the raw data from the on-premises sources to Amazon S3, such as AWS DataSync, AWS Storage Gateway, AWS Snowball, or AWS Direct Connect. Amazon S3 is a durable, scalable, and secure object storage service that can store any amount and type of data.
Create an AWS Glue ETL job to perform the ETL processing against the input data: The Data Scientist can use the AWS Glue console, AWS Glue API, AWS SDK, or AWS CLI to create and configure an AWS Glue ETL job. The Data Scientist can specify the input and output data sources, the IAM role, the security configuration, the job parameters, and the PySpark script location. The Data Scientist can also use the AWS Glue Studio, which is a graphical interface that can help design, run, and monitor ETL jobs visually.
Write the ETL job in PySpark to leverage the existing logic: The Data Scientist can use a code editor of their choice to write the ETL script in PySpark, using the existing logic to transform the data. The Data Scientist can also use the AWS Glue script editor, which is an integrated development environment (IDE) that can help write, debug, and test the ETL code. The Data Scientist can store the ETL script in Amazon S3 or GitHub, and reference it in the AWS Glue ETL job configuration.
Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule: The Data Scientist can use the AWS Glue console, AWS Glue API, AWS SDK, or AWS CLI to create and configure an AWS Glue trigger. The Data Scientist can specify the name, type, and schedule of the trigger, and associate it with the AWS Glue ETL job. The trigger will start the ETL job according to the defined schedule.
Configure the output target of the ETL job to write to a “processed” location in Amazon S3 that is accessible for downstream use: The Data Scientist can specify the output location of the ETL job in the PySpark script, using the AWS Glue DynamicFrame or Spark DataFrame APIs. The Data Scientist can write the output data to a “processed” location in Amazon S3, using a format such as Parquet, ORC, JSON, or CSV, that is suitable for downstream processing.
References:
What Is AWS Glue?
AWS Glue Components
AWS Glue Studio
AWS Glue Triggers
B
Explanation:
The Data Scientist needs to migrate an existing on-premises ETL process to the cloud, using a solution that can combine multiple data sources, reuse existing PySpark logic, run on the existing schedule, and minimize the number of servers that need to be managed. The best architecture for this scenario is to use AWS Glue, which is a serverless data integration service that can create and run ETL jobs on AWS.
AWS Glue can perform the following tasks to meet the requirements:
Combine multiple data sources: AWS Glue can access data from various sources, such as Amazon S3, Amazon RDS, Amazon Redshift, Amazon DynamoDB, and more. AWS Glue can also crawl the data sources and discover their schemas, formats, and partitions, and store them in the AWS Glue Data Catalog, which is a centralized metadata repository for all the data assets.
Reuse existing PySpark logic: AWS Glue supports writing ETL scripts in Python or Scala, using Apache Spark as the underlying execution engine. AWS Glue provides a library of built-in transformations and connectors that can simplify the ETL code. The Data Scientist can write the ETL job in PySpark and leverage the existing logic to perform the data processing.
Run the solution on the existing schedule: AWS Glue can create triggers that can start ETL jobs based on a schedule, an event, or a condition. The Data Scientist can create a new AWS Glue trigger to run the ETL job based on the existing schedule, using a cron expression or a relative time interval.
Minimize the number of servers that need to be managed: AWS Glue is a serverless service, which means that it automatically provisions, configures, scales, and manages the compute resources
required to run the ETL jobs. The Data Scientist does not need to worry about setting up, maintaining, or monitoring any servers or clusters for the ETL process.
Therefore, the Data Scientist should use the following architecture to build the cloud solution: Write the raw data to Amazon S3: The Data Scientist can use any method to upload the raw data from the on-premises sources to Amazon S3, such as AWS DataSync, AWS Storage Gateway, AWS Snowball, or AWS Direct Connect. Amazon S3 is a durable, scalable, and secure object storage service that can store any amount and type of data.
Create an AWS Glue ETL job to perform the ETL processing against the input data: The Data Scientist can use the AWS Glue console, AWS Glue API, AWS SDK, or AWS CLI to create and configure an AWS Glue ETL job. The Data Scientist can specify the input and output data sources, the IAM role, the security configuration, the job parameters, and the PySpark script location. The Data Scientist can also use the AWS Glue Studio, which is a graphical interface that can help design, run, and monitor ETL jobs visually.
Write the ETL job in PySpark to leverage the existing logic: The Data Scientist can use a code editor of their choice to write the ETL script in PySpark, using the existing logic to transform the data. The Data Scientist can also use the AWS Glue script editor, which is an integrated development environment (IDE) that can help write, debug, and test the ETL code. The Data Scientist can store the ETL script in Amazon S3 or GitHub, and reference it in the AWS Glue ETL job configuration.
Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule: The Data Scientist can use the AWS Glue console, AWS Glue API, AWS SDK, or AWS CLI to create and configure an AWS Glue trigger. The Data Scientist can specify the name, type, and schedule of the trigger, and associate it with the AWS Glue ETL job. The trigger will start the ETL job according to the defined schedule.
Configure the output target of the ETL job to write to a “processed” location in Amazon S3 that is accessible for downstream use: The Data Scientist can specify the output location of the ETL job in the PySpark script, using the AWS Glue DynamicFrame or Spark DataFrame APIs. The Data Scientist can write the output data to a “processed” location in Amazon S3, using a format such as Parquet, ORC, JSON, or CSV, that is suitable for downstream processing.
References:
What Is AWS Glue?
AWS Glue Components
AWS Glue Studio
AWS Glue Triggers
Question #40
A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.
How should a data scientist adjust the solution?
- A . Use the event tracker in Amazon Personalize to include real-time user interactions.
- B . Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.
- C . Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.
- D . Add event type and event value fields to the interactions dataset in Amazon Personalize.
Correct Answer: A
A
Explanation:
The best option is to use the event tracker in Amazon Personalize to include real-time user interactions. This will allow the model to learn from the feedback of the customers during the marketing campaign and adjust the recommendations accordingly. The event tracker can capture click-through, add-to-cart, purchase, and other types of events that indicate the user’s preferences. By using the event tracker, the company can improve the relevance and freshness of the recommendations and avoid the decrease in sales.
The other options are not as effective as using the event tracker. Adding user metadata and using the HRNN-Metadata recipe in Amazon Personalize can help capture the user’s attributes and preferences, but it will not reflect the changes in user behavior during the marketing campaign. Implementing a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker can also provide personalized recommendations, but it will require more time and effort to train and deploy the model. Adding event type and event value fields to the interactions dataset in Amazon Personalize can help capture the importance and context of each interaction, but it will not update the model with the latest user feedback.
References:
Recording events – Amazon Personalize
Using real-time events – Amazon Personalize
A
Explanation:
The best option is to use the event tracker in Amazon Personalize to include real-time user interactions. This will allow the model to learn from the feedback of the customers during the marketing campaign and adjust the recommendations accordingly. The event tracker can capture click-through, add-to-cart, purchase, and other types of events that indicate the user’s preferences. By using the event tracker, the company can improve the relevance and freshness of the recommendations and avoid the decrease in sales.
The other options are not as effective as using the event tracker. Adding user metadata and using the HRNN-Metadata recipe in Amazon Personalize can help capture the user’s attributes and preferences, but it will not reflect the changes in user behavior during the marketing campaign. Implementing a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker can also provide personalized recommendations, but it will require more time and effort to train and deploy the model. Adding event type and event value fields to the interactions dataset in Amazon Personalize can help capture the importance and context of each interaction, but it will not update the model with the latest user feedback.
References:
Recording events – Amazon Personalize
Using real-time events – Amazon Personalize