Amazon MLS-C01 Practice Exams
Last updated on Oct 23,2026- Exam Code: MLS-C01
- Exam Name: AWS Certified Machine Learning - Specialty
- Certification Provider: Amazon
- Latest update: Oct 23,2026
Question #271
A company needs to deploy a chatbot to answer common questions from customers. The chatbot must base its answers on company documentation.
Which solution will meet these requirements with the LEAST development effort?
- A . Index company documents by using Amazon Kendra. Integrate the chatbot with Amazon Kendra by using the Amazon Kendra Query API operation to answer customer questions.
- B . Train a Bidirectional Attention Flow (BiDAF) network based on past customer questions and company documents. Deploy the model as a real-time Amazon SageMaker endpoint. Integrate the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation to answer customer questions.
- C . Train an Amazon SageMaker BlazingText model based on past customer questions and company documents. Deploy the model as a real-time SageMaker endpoint. Integrate the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation to answer customer questions.
- D . Index company documents by using Amazon OpenSearch Service. Integrate the chatbot with OpenSearch Service by using the OpenSearch Service k-nearest neighbors (k-NN) Query API operation to answer customer questions.
Correct Answer: A
A
Explanation:
The solution A will meet the requirements with the least development effort because it uses Amazon Kendra, which is a highly accurate and easy to use intelligent search service powered by machine learning. Amazon Kendra can index company documents from various sources and formats, such as PDF, HTML, Word, and more. Amazon Kendra can also integrate with chatbots by using the Amazon Kendra Query API operation, which can understand natural language questions and provide relevant answers from the indexed documents. Amazon Kendra can also provide additional information, such as document excerpts, links, and FAQs, to enhance the chatbot experience1.
The other options are not suitable because:
Option B: Training a Bidirectional Attention Flow (BiDAF) network based on past customer questions and company documents, deploying the model as a real-time Amazon SageMaker endpoint, and integrating the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation will incur more development effort than using Amazon Kendra. The company will have to write the code for the BiDAF network, which is a complex deep learning model for question answering. The company will also have to manage the SageMaker endpoint, the model artifact, and the inference logic2.
Option C: Training an Amazon SageMaker BlazingText model based on past customer questions and company documents, deploying the model as a real-time SageMaker endpoint, and integrating the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation will incur more development effort than using Amazon Kendra. The company will have to write the code for the BlazingText model, which is a fast and scalable text classification and word embedding algorithm. The company will also have to manage the SageMaker endpoint, the model artifact, and the inference logic3.
Option D: Indexing company documents by using Amazon OpenSearch Service and integrating the chatbot with OpenSearch Service by using the OpenSearch Service k-nearest neighbors (k-NN) Query API operation will not meet the requirements effectively. Amazon OpenSearch Service is a fully managed service that provides fast and scalable search and analytics capabilities. However, it is not designed for natural language question answering, and it may not provide accurate or relevant answers for the chatbot. Moreover, the k-NN Query API operation is used to find the most similar documents or vectors based on a distance function, not to find the best answers based on a natural language query4.
References:
1: Amazon Kendra
2: Bidirectional Attention Flow for Machine Comprehension
3: Amazon SageMaker BlazingText
4: Amazon OpenSearch Service
A
Explanation:
The solution A will meet the requirements with the least development effort because it uses Amazon Kendra, which is a highly accurate and easy to use intelligent search service powered by machine learning. Amazon Kendra can index company documents from various sources and formats, such as PDF, HTML, Word, and more. Amazon Kendra can also integrate with chatbots by using the Amazon Kendra Query API operation, which can understand natural language questions and provide relevant answers from the indexed documents. Amazon Kendra can also provide additional information, such as document excerpts, links, and FAQs, to enhance the chatbot experience1.
The other options are not suitable because:
Option B: Training a Bidirectional Attention Flow (BiDAF) network based on past customer questions and company documents, deploying the model as a real-time Amazon SageMaker endpoint, and integrating the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation will incur more development effort than using Amazon Kendra. The company will have to write the code for the BiDAF network, which is a complex deep learning model for question answering. The company will also have to manage the SageMaker endpoint, the model artifact, and the inference logic2.
Option C: Training an Amazon SageMaker BlazingText model based on past customer questions and company documents, deploying the model as a real-time SageMaker endpoint, and integrating the model with the chatbot by using the SageMaker Runtime InvokeEndpoint API operation will incur more development effort than using Amazon Kendra. The company will have to write the code for the BlazingText model, which is a fast and scalable text classification and word embedding algorithm. The company will also have to manage the SageMaker endpoint, the model artifact, and the inference logic3.
Option D: Indexing company documents by using Amazon OpenSearch Service and integrating the chatbot with OpenSearch Service by using the OpenSearch Service k-nearest neighbors (k-NN) Query API operation will not meet the requirements effectively. Amazon OpenSearch Service is a fully managed service that provides fast and scalable search and analytics capabilities. However, it is not designed for natural language question answering, and it may not provide accurate or relevant answers for the chatbot. Moreover, the k-NN Query API operation is used to find the most similar documents or vectors based on a distance function, not to find the best answers based on a natural language query4.
References:
1: Amazon Kendra
2: Bidirectional Attention Flow for Machine Comprehension
3: Amazon SageMaker BlazingText
4: Amazon OpenSearch Service
Question #272
A financial services company wants to automate its loan approval process by building a machine learning (ML) model. Each loan data point contains credit history from a third-party data source and demographic information about the customer. Each loan approval prediction must come with a report that contains an explanation for why the customer was approved for a loan or was denied for a loan. The company will use Amazon SageMaker to build the model.
Which solution will meet these requirements with the LEAST development effort?
- A . Use SageMaker Model Debugger to automatically debug the predictions, generate the explanation, and attach the explanation report.
- B . Use AWS Lambda to provide feature importance and partial dependence plots. Use the plots to generate and attach the explanation report.
- C . Use SageMaker Clarify to generate the explanation report. Attach the report to the predicted results.
- D . Use custom Amazon Cloud Watch metrics to generate the explanation report. Attach the report to the predicted results.
Correct Answer: C
C
Explanation:
The best solution for this scenario is to use SageMaker Clarify to generate the explanation report and attach it to the predicted results. SageMaker Clarify provides tools to help explain how machine learning (ML) models make predictions using a model-agnostic feature attribution approach based on SHAP values. It can also detect and measure potential bias in the data and the model. SageMaker Clarify can generate explanation reports during data preparation, model training, and model deployment. The reports include metrics, graphs, and examples that help understand the model behavior and predictions. The reports can be attached to the predicted results using the SageMaker SDK or the SageMaker API.
The other solutions are less optimal because they require more development effort and additional services. Using SageMaker Model Debugger would require modifying the training script to save the model output tensors and writing custom rules to debug and explain the predictions. Using AWS Lambda would require writing code to invoke the ML model, compute the feature importance and partial dependence plots, and generate and attach the explanation report. Using custom Amazon CloudWatch metrics would require writing code to publish the metrics, create dashboards, and generate and attach the explanation report.
References:
Bias Detection and Model Explainability – Amazon SageMaker Clarify – AWS Amazon SageMaker Clarify Model Explainability
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability
GitHub – aws/amazon-sagemaker-clarify: Fairness Aware Machine Learning
C
Explanation:
The best solution for this scenario is to use SageMaker Clarify to generate the explanation report and attach it to the predicted results. SageMaker Clarify provides tools to help explain how machine learning (ML) models make predictions using a model-agnostic feature attribution approach based on SHAP values. It can also detect and measure potential bias in the data and the model. SageMaker Clarify can generate explanation reports during data preparation, model training, and model deployment. The reports include metrics, graphs, and examples that help understand the model behavior and predictions. The reports can be attached to the predicted results using the SageMaker SDK or the SageMaker API.
The other solutions are less optimal because they require more development effort and additional services. Using SageMaker Model Debugger would require modifying the training script to save the model output tensors and writing custom rules to debug and explain the predictions. Using AWS Lambda would require writing code to invoke the ML model, compute the feature importance and partial dependence plots, and generate and attach the explanation report. Using custom Amazon CloudWatch metrics would require writing code to publish the metrics, create dashboards, and generate and attach the explanation report.
References:
Bias Detection and Model Explainability – Amazon SageMaker Clarify – AWS Amazon SageMaker Clarify Model Explainability
Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability
GitHub – aws/amazon-sagemaker-clarify: Fairness Aware Machine Learning
Question #273
A Machine Learning Specialist is building a supervised model that will evaluate customers’ satisfaction with their mobile phone service based on recent usage. The model’s output should infer whether or not a customer is likely to switch to a competitor in the next 30 days.
Which of the following modeling techniques should the Specialist use1?
- A . Time-series prediction
- B . Anomaly detection
- C . Binary classification
- D . Regression
Correct Answer: C
C
Explanation:
The modeling technique that the Machine Learning Specialist should use is binary classification. Binary classification is a type of supervised learning that predicts whether an input belongs to one of two possible classes. In this case, the input is the customer’s recent usage data and the output is whether or not the customer is likely to switch to a competitor in the next 30 days. This is a binary outcome, either yes or no, so binary classification is suitable for this problem. The other options are not appropriate for this problem. Time-series prediction is a type of supervised learning that forecasts future values based on past and present data. Anomaly detection is a type of unsupervised learning that identifies outliers or abnormal patterns in the data. Regression is a type of supervised learning that estimates a continuous numerical value based on the input features.
References: Binary Classification, Time Series Prediction, Anomaly Detection, Regression
C
Explanation:
The modeling technique that the Machine Learning Specialist should use is binary classification. Binary classification is a type of supervised learning that predicts whether an input belongs to one of two possible classes. In this case, the input is the customer’s recent usage data and the output is whether or not the customer is likely to switch to a competitor in the next 30 days. This is a binary outcome, either yes or no, so binary classification is suitable for this problem. The other options are not appropriate for this problem. Time-series prediction is a type of supervised learning that forecasts future values based on past and present data. Anomaly detection is a type of unsupervised learning that identifies outliers or abnormal patterns in the data. Regression is a type of supervised learning that estimates a continuous numerical value based on the input features.
References: Binary Classification, Time Series Prediction, Anomaly Detection, Regression
Question #274
A web-based company wants to improve its conversion rate on its landing page Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker However there is an overfitting problem training data shows 90% accuracy in predictions, while test data shows 70% accuracy only
The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases
Which action is recommended to provide the HIGHEST accuracy model for the company’s test and validation data?
- A . Increase the randomization of training data in the mini-batches used in training.
- B . Allocate a higher proportion of the overall data to the training dataset
- C . Apply L1 or L2 regularization and dropouts to the training.
- D . Reduce the number of layers and units (or neurons) from the deep learning network.
Correct Answer: C
C
Explanation:
Regularization and dropouts are techniques that can help reduce overfitting in deep learning models. Overfitting occurs when the model learns too much from the training data and fails to generalize well to new data. Regularization adds a penalty term to the loss function that penalizes the model for having large or complex weights. This prevents the model from memorizing the noise or irrelevant features in the training data. L1 and L2 are two types of regularization that differ in how they calculate the penalty term. L1 regularization uses the absolute value of the weights, while L2 regularization uses the square of the weights. Dropouts are another technique that randomly drops out some units or neurons from the network during training. This creates a thinner network that is ess prone to overfitting. Dropouts also act as a form of ensemble learning, where multiple sub-models are combined to produce a better prediction. By applying regularization and dropouts to the training, the web-based company can improve the generalization and accuracy of its deep learning model on the test and validation data.
References:
Regularization: A video that explains the concept and benefits of regularization in deep learning.
Dropout: A video that demonstrates how dropout works and why it helps reduce overfitting.
C
Explanation:
Regularization and dropouts are techniques that can help reduce overfitting in deep learning models. Overfitting occurs when the model learns too much from the training data and fails to generalize well to new data. Regularization adds a penalty term to the loss function that penalizes the model for having large or complex weights. This prevents the model from memorizing the noise or irrelevant features in the training data. L1 and L2 are two types of regularization that differ in how they calculate the penalty term. L1 regularization uses the absolute value of the weights, while L2 regularization uses the square of the weights. Dropouts are another technique that randomly drops out some units or neurons from the network during training. This creates a thinner network that is ess prone to overfitting. Dropouts also act as a form of ensemble learning, where multiple sub-models are combined to produce a better prediction. By applying regularization and dropouts to the training, the web-based company can improve the generalization and accuracy of its deep learning model on the test and validation data.
References:
Regularization: A video that explains the concept and benefits of regularization in deep learning.
Dropout: A video that demonstrates how dropout works and why it helps reduce overfitting.
Question #275
An Amazon SageMaker notebook instance is launched into Amazon VPC. The SageMaker notebook references data contained in an Amazon S3 bucket in another account. The bucket is encrypted using SSE-KMS. The instance returns an access denied error when trying to access data in Amazon S3.
Which of the following are required to access the bucket and avoid the access denied error? (Select THREE)
- A . An AWS KMS key policy that allows access to the customer master key (CMK)
- B . A SageMaker notebook security group that allows access to Amazon S3
- C . An 1AM role that allows access to the specific S3 bucket
- D . A permissive S3 bucket policy
- E . An S3 bucket owner that matches the notebook owner
- F . A SegaMaker notebook subnet ACL that allow traffic to Amazon S3.
Correct Answer: A, B, C
A, B, C
Explanation:
To access an Amazon S3 bucket in another account that is encrypted using SSE-KMS, the following are required:
A) An AWS KMS key policy that allows access to the customer master key (CMK). The CMK is the encryption key that is used to encrypt and decrypt the data in the S3 bucket. The KMS key policy defines who can use and manage the CMK. To allow access to the CMK from another account, the key policy must include a statement that grants the necessary permissions (such as kms:Decrypt) to the principal from the other account (such as the SageMaker notebook IAM role).
B) A SageMaker notebook security group that allows access to Amazon S3. A security group is a virtual firewall that controls the inbound and outbound traffic for the SageMaker notebook instance. To allow the notebook instance to access the S3 bucket, the security group must have a rule that allows outbound traffic to the S3 endpoint on port 443 (HTTPS).
C) An IAM role that allows access to the specific S3 bucket. An IAM role is an identity that can be assumed by the SageMaker notebook instance to access AWS resources. The IAM role must have a policy that grants the necessary permissions (such as s3:GetObject) to access the specific S3 bucket. The policy must also include a condition that allows access to the CMK in the other account.
The following are not required or correct:
D) A permissive S3 bucket policy. A bucket policy is a resource-based policy that defines who can access the S3 bucket and what actions they can perform. A permissive bucket policy is not required and not recommended, as it can expose the bucket to unauthorized access. A bucket policy should follow the principle of least privilege and grant the minimum permissions necessary to the specific principals that need access.
E) An S3 bucket owner that matches the notebook owner. The S3 bucket owner and the notebook owner do not need to match, as long as the bucket owner grants cross-account access to the notebook owner through the KMS key policy and the bucket policy (if applicable).
F) A SegaMaker notebook subnet ACL that allow traffic to Amazon S3. A subnet ACL is a network access control list that acts as an optional layer of security for the SageMaker notebook instance’s subnet. A subnet ACL is not required to access the S3 bucket, as the security group is sufficient to control the traffic. However, if a subnet ACL is used, it must not block the traffic to the S3 endpoint.
A, B, C
Explanation:
To access an Amazon S3 bucket in another account that is encrypted using SSE-KMS, the following are required:
A) An AWS KMS key policy that allows access to the customer master key (CMK). The CMK is the encryption key that is used to encrypt and decrypt the data in the S3 bucket. The KMS key policy defines who can use and manage the CMK. To allow access to the CMK from another account, the key policy must include a statement that grants the necessary permissions (such as kms:Decrypt) to the principal from the other account (such as the SageMaker notebook IAM role).
B) A SageMaker notebook security group that allows access to Amazon S3. A security group is a virtual firewall that controls the inbound and outbound traffic for the SageMaker notebook instance. To allow the notebook instance to access the S3 bucket, the security group must have a rule that allows outbound traffic to the S3 endpoint on port 443 (HTTPS).
C) An IAM role that allows access to the specific S3 bucket. An IAM role is an identity that can be assumed by the SageMaker notebook instance to access AWS resources. The IAM role must have a policy that grants the necessary permissions (such as s3:GetObject) to access the specific S3 bucket. The policy must also include a condition that allows access to the CMK in the other account.
The following are not required or correct:
D) A permissive S3 bucket policy. A bucket policy is a resource-based policy that defines who can access the S3 bucket and what actions they can perform. A permissive bucket policy is not required and not recommended, as it can expose the bucket to unauthorized access. A bucket policy should follow the principle of least privilege and grant the minimum permissions necessary to the specific principals that need access.
E) An S3 bucket owner that matches the notebook owner. The S3 bucket owner and the notebook owner do not need to match, as long as the bucket owner grants cross-account access to the notebook owner through the KMS key policy and the bucket policy (if applicable).
F) A SegaMaker notebook subnet ACL that allow traffic to Amazon S3. A subnet ACL is a network access control list that acts as an optional layer of security for the SageMaker notebook instance’s subnet. A subnet ACL is not required to access the S3 bucket, as the security group is sufficient to control the traffic. However, if a subnet ACL is used, it must not block the traffic to the S3 endpoint.
Question #276
A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable
What should be done to reduce the impact of having such a large number of features?
- A . Perform one-hot encoding on highly correlated features
- B . Use matrix multiplication on highly correlated features.
- C . Create a new feature space using principal component analysis (PCA)
- D . Apply the Pearson correlation coefficient
Correct Answer: C
C
Explanation:
Principal component analysis (PCA) is an unsupervised machine learning algorithm that attempts to reduce the dimensionality (number of features) within a dataset while still retaining as much information as possible. This is done by finding a new set of features called components, which are composites of the original features that are uncorrelated with one another. They are also constrained so that the first component accounts for the largest possible variability in the data, the second component the second most variability, and so on. By using PCA, the impact of having a large number of features that are highly correlated with each other can be reduced, as the new feature space will have fewer dimensions and less redundancy. This can make the linear models more stable and less prone to overfitting.
References:
Principal Component Analysis (PCA) Algorithm – Amazon SageMaker
Perform a large-scale principal component analysis faster using Amazon SageMaker | AWS Machine Learning Blog
Machine Learning- Prinicipal Component Analysis | i2tutorials
C
Explanation:
Principal component analysis (PCA) is an unsupervised machine learning algorithm that attempts to reduce the dimensionality (number of features) within a dataset while still retaining as much information as possible. This is done by finding a new set of features called components, which are composites of the original features that are uncorrelated with one another. They are also constrained so that the first component accounts for the largest possible variability in the data, the second component the second most variability, and so on. By using PCA, the impact of having a large number of features that are highly correlated with each other can be reduced, as the new feature space will have fewer dimensions and less redundancy. This can make the linear models more stable and less prone to overfitting.
References:
Principal Component Analysis (PCA) Algorithm – Amazon SageMaker
Perform a large-scale principal component analysis faster using Amazon SageMaker | AWS Machine Learning Blog
Machine Learning- Prinicipal Component Analysis | i2tutorials
Question #277
A manufacturing company asks its Machine Learning Specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100000 images per defect type for training During the injial training of the image classification model the Specialist notices that the validation accuracy is 80%, while the training accuracy is 90% It is known that human-level performance for this type of image classification is around 90%.
What should the Specialist consider to fix this issue1?
- A . A longer training time
- B . Making the network larger
- C . Using a different optimizer
- D . Using some form of regularization
Correct Answer: D
D
Explanation:
Regularization is a technique that can be used to prevent overfitting and improve model performance on unseen data. Overfitting occurs when the model learns the training data too well and fails to generalize to new and unseen data. This can be seen in the question, where the validation accuracy is lower than the training accuracy, and both are lower than the human-level performance. Regularization is a way of adding some constraints or penalties to the model to reduce its complexity and prevent it from memorizing the training data. Some common forms of regularization for image classification are:
Weight decay: Adding a term to the loss function that penalizes large weights in the model. This can help reduce the variance and noise in the model and make it more robust to small changes in the input.
Dropout: Randomly dropping out some units or connections in the model during training. This can help reduce the co-dependency among the units and make the model more resilient to missing or corrupted features.
Data augmentation: Artificially increasing the size and diversity of the training data by applying random transformations, such as cropping, flipping, rotating, scaling, etc. This can help the model learn more invariant and generalizable features and reduce the risk of overfitting to specific patterns in the training data.
The other options are not likely to fix the issue of overfitting, and may even worsen it:
A longer training time: This can lead to more overfitting, as the model will have more chances to fit the noise and details in the training data that are not relevant for the validation data.
Making the network larger: This can increase the model capacity and complexity, which can also lead
to more overfitting, as the model will have more parameters to learn and adjust to the training data.
Using a different optimizer: This can affect the speed and stability of the training process, but not
necessarily the generalization ability of the model. The choice of optimizer depends on the
characteristics of the data and the model, and there is no guarantee that a different optimizer will
prevent overfitting.
References:
Regularization (machine learning)
Image Classification: Regularization
How to Reduce Overfitting With Dropout Regularization in Keras
D
Explanation:
Regularization is a technique that can be used to prevent overfitting and improve model performance on unseen data. Overfitting occurs when the model learns the training data too well and fails to generalize to new and unseen data. This can be seen in the question, where the validation accuracy is lower than the training accuracy, and both are lower than the human-level performance. Regularization is a way of adding some constraints or penalties to the model to reduce its complexity and prevent it from memorizing the training data. Some common forms of regularization for image classification are:
Weight decay: Adding a term to the loss function that penalizes large weights in the model. This can help reduce the variance and noise in the model and make it more robust to small changes in the input.
Dropout: Randomly dropping out some units or connections in the model during training. This can help reduce the co-dependency among the units and make the model more resilient to missing or corrupted features.
Data augmentation: Artificially increasing the size and diversity of the training data by applying random transformations, such as cropping, flipping, rotating, scaling, etc. This can help the model learn more invariant and generalizable features and reduce the risk of overfitting to specific patterns in the training data.
The other options are not likely to fix the issue of overfitting, and may even worsen it:
A longer training time: This can lead to more overfitting, as the model will have more chances to fit the noise and details in the training data that are not relevant for the validation data.
Making the network larger: This can increase the model capacity and complexity, which can also lead
to more overfitting, as the model will have more parameters to learn and adjust to the training data.
Using a different optimizer: This can affect the speed and stability of the training process, but not
necessarily the generalization ability of the model. The choice of optimizer depends on the
characteristics of the data and the model, and there is no guarantee that a different optimizer will
prevent overfitting.
References:
Regularization (machine learning)
Image Classification: Regularization
How to Reduce Overfitting With Dropout Regularization in Keras
Question #278
A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS. The application collects device usage information from device users. The company’s Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company’s devices. Predictions from this model are used by a downstream application that determines the best approach for contacting users.
The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company’s business goals. To measure long-term effectiveness, the team wants to run multiple versions of the model in parallel for long periods of time, with the ability to control the portion of inferences served by the models.
Which solution satisfies these requirements with MINIMAL effort?
- A . Build and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one for each model. Programmatically control invoking different models for inference at the application layer.
- B . Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configuration with multiple production variants. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.
- C . Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical devices. Programmatically control which model is invoked for inference based on the medical device type.
- D . Build and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple models. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.
Correct Answer: B
B
Explanation:
Amazon SageMaker is a service that allows users to build, train, and deploy ML models on AWS. Amazon SageMaker endpoints are scalable and secure web services that can be used to perform real-time inference on ML models. An endpoint configuration defines the models that are deployed and the resources that are used by the endpoint. An endpoint configuration can have multiple production variants, each representing a different version or variant of a model. Users can specify the portion of the inferences served by each production variant using the initialVariantWeight parameter. Users can also programmatically update the endpoint configuration to change the portion of the inferences served by each production variant using the UpdateEndpointWeightsAndCapacities API. Therefore, option B is the best solution to satisfy the requirements with minimal effort.
Option A is incorrect because creating multiple endpoints for each model would incur more cost and complexity than using a single endpoint with multiple production variants. Moreover, controlling the invocation of different models at the application layer would require more custom logic and coordination than using the UpdateEndpointWeightsAndCapacities API. Option C is incorrect because Amazon SageMaker Neo is a service that allows users to optimize ML models for different hardware platforms, such as edge devices. It is not relevant to the problem of running multiple
versions of a model in parallel for long periods of time. Option D is incorrect because Amazon SageMaker batch transform is a service that allows users to perform asynchronous inference on large datasets. It is not suitable for the problem of performing real-time inference on streaming data from device users.
References:
Deploying models to Amazon SageMaker hosting services – Amazon SageMaker
Update an Amazon SageMaker endpoint to accommodate new models – Amazon SageMaker UpdateEndpointWeightsAndCapacities – Amazon SageMaker
B
Explanation:
Amazon SageMaker is a service that allows users to build, train, and deploy ML models on AWS. Amazon SageMaker endpoints are scalable and secure web services that can be used to perform real-time inference on ML models. An endpoint configuration defines the models that are deployed and the resources that are used by the endpoint. An endpoint configuration can have multiple production variants, each representing a different version or variant of a model. Users can specify the portion of the inferences served by each production variant using the initialVariantWeight parameter. Users can also programmatically update the endpoint configuration to change the portion of the inferences served by each production variant using the UpdateEndpointWeightsAndCapacities API. Therefore, option B is the best solution to satisfy the requirements with minimal effort.
Option A is incorrect because creating multiple endpoints for each model would incur more cost and complexity than using a single endpoint with multiple production variants. Moreover, controlling the invocation of different models at the application layer would require more custom logic and coordination than using the UpdateEndpointWeightsAndCapacities API. Option C is incorrect because Amazon SageMaker Neo is a service that allows users to optimize ML models for different hardware platforms, such as edge devices. It is not relevant to the problem of running multiple
versions of a model in parallel for long periods of time. Option D is incorrect because Amazon SageMaker batch transform is a service that allows users to perform asynchronous inference on large datasets. It is not suitable for the problem of performing real-time inference on streaming data from device users.
References:
Deploying models to Amazon SageMaker hosting services – Amazon SageMaker
Update an Amazon SageMaker endpoint to accommodate new models – Amazon SageMaker UpdateEndpointWeightsAndCapacities – Amazon SageMaker
Question #279
A company is using Amazon Polly to translate plaintext documents to speech for automated company announcements However company acronyms are being mispronounced in the current documents.
How should a Machine Learning Specialist address this issue for future documents?
- A . Convert current documents to SSML with pronunciation tags
- B . Create an appropriate pronunciation lexicon.
- C . Output speech marks to guide in pronunciation
- D . Use Amazon Lex to preprocess the text files for pronunciation
Correct Answer: B
B
Explanation:
A pronunciation lexicon is a file that defines how words or phrases should be pronounced by Amazon Polly. A lexicon can help customize the speech output for words that are uncommon, foreign, or have multiple pronunciations. A lexicon must conform to the Pronunciation Lexicon Specification (PLS) standard and can be stored in an AWS region using the Amazon Polly API. To use a lexicon for synthesizing speech, the lexicon name must be specified in the <speak> SSML tag.
For example, the following lexicon defines how to pronounce the acronym W3C:
<lexicon version=“1.0” xmlns=“http://www.w3.org/2005/01/pronunciation-lexicon” alphabet=“ipa” xml:lang=“en-US”> <lexeme> <grapheme>W3C</grapheme> <alias>World Wide Web Consortium</alias> </lexeme> </lexicon>
To use this lexicon, the text input must include the following SSML tag:
<speak version=“1.1” xmlns=“http://www.w3.org/2001/10/synthesis” xml:lang=“en-US”> <voice name=“Joanna”> <lexicon name=“w3c_lexicon”/>. The <say-as interpret-as=“characters”>W3C</say-as> is an international community that develops open standards to ensure the long-term growth of the Web. </voice> </speak>
References:
Customize pronunciation using lexicons in Amazon Polly: A blog post that explains how to use lexicons for creating custom pronunciations.
Managing Lexicons: A documentation page that describes how to store and retrieve lexicons using the Amazon Polly API.
B
Explanation:
A pronunciation lexicon is a file that defines how words or phrases should be pronounced by Amazon Polly. A lexicon can help customize the speech output for words that are uncommon, foreign, or have multiple pronunciations. A lexicon must conform to the Pronunciation Lexicon Specification (PLS) standard and can be stored in an AWS region using the Amazon Polly API. To use a lexicon for synthesizing speech, the lexicon name must be specified in the <speak> SSML tag.
For example, the following lexicon defines how to pronounce the acronym W3C:
<lexicon version=“1.0” xmlns=“http://www.w3.org/2005/01/pronunciation-lexicon” alphabet=“ipa” xml:lang=“en-US”> <lexeme> <grapheme>W3C</grapheme> <alias>World Wide Web Consortium</alias> </lexeme> </lexicon>
To use this lexicon, the text input must include the following SSML tag:
<speak version=“1.1” xmlns=“http://www.w3.org/2001/10/synthesis” xml:lang=“en-US”> <voice name=“Joanna”> <lexicon name=“w3c_lexicon”/>. The <say-as interpret-as=“characters”>W3C</say-as> is an international community that develops open standards to ensure the long-term growth of the Web. </voice> </speak>
References:
Customize pronunciation using lexicons in Amazon Polly: A blog post that explains how to use lexicons for creating custom pronunciations.
Managing Lexicons: A documentation page that describes how to store and retrieve lexicons using the Amazon Polly API.
Question #280
A company processes millions of orders every day. The company uses Amazon DynamoDB tables to store order information. When customers submit new orders, the new orders are immediately added to the DynamoDB tables. New orders arrive in the DynamoDB tables continuously.
A data scientist must build a peak-time prediction solution. The data scientist must also create an Amazon OuickSight dashboard to display near real-lime order insights. The data scientist needs to build a solution that will give QuickSight access to the data as soon as new order information arrives.
Which solution will meet these requirements with the LEAST delay between when a new order is processed and when QuickSight can access the new order information?
- A . Use AWS Glue to export the data from Amazon DynamoDB to Amazon S3. Configure OuickSight to access the data in Amazon S3.
- B . Use Amazon Kinesis Data Streams to export the data from Amazon DynamoDB to Amazon S3. Configure OuickSight to access the data in Amazon S3.
- C . Use an API call from OuickSight to access the data that is in Amazon DynamoDB directly
- D . Use Amazon Kinesis Data Firehose to export the data from Amazon DynamoDB to Amazon S3. Configure OuickSight to access the data in Amazon S3.
Correct Answer: B
B
Explanation:
The best solution for this scenario is to use Amazon Kinesis Data Streams to export the data from Amazon DynamoDB to Amazon S3, and then configure QuickSight to access the data in Amazon S3.
This solution has the following advantages:
It allows near real-time data ingestion from DynamoDB to S3 using Kinesis Data Streams, which can capture and process data continuously and at scale1.
It enables QuickSight to access the data in S3 using the Athena connector, which supports federated queries to multiple data sources, including Kinesis Data Streams2.
It avoids the need to create and manage a Lambda function or a Glue crawler, which are required for the other solutions.
The other solutions have the following drawbacks:
Using AWS Glue to export the data from DynamoDB to S3 introduces additional latency and complexity, as Glue is a batch-oriented service that requires scheduling and configuration3. Using an API call from QuickSight to access the data in DynamoDB directly is not possible, as QuickSight does not support direct querying of DynamoDB4.
Using Kinesis Data Firehose to export the data from DynamoDB to S3 is less efficient and flexible than using Kinesis Data Streams, as Firehose does not support custom data processing or transformation, and has a minimum buffer interval of 60 seconds5.
References:
1: Amazon Kinesis Data Streams – Amazon Web Services
2: Visualize Amazon DynamoDB insights in Amazon QuickSight using the Amazon Athena DynamoDB connector and AWS Glue | AWS Big Data Blog
3: AWS Glue – Amazon Web Services
4: Visualising your Amazon DynamoDB data with Amazon QuickSight – DEV Community
5: Amazon Kinesis Data Firehose – Amazon Web Services
B
Explanation:
The best solution for this scenario is to use Amazon Kinesis Data Streams to export the data from Amazon DynamoDB to Amazon S3, and then configure QuickSight to access the data in Amazon S3.
This solution has the following advantages:
It allows near real-time data ingestion from DynamoDB to S3 using Kinesis Data Streams, which can capture and process data continuously and at scale1.
It enables QuickSight to access the data in S3 using the Athena connector, which supports federated queries to multiple data sources, including Kinesis Data Streams2.
It avoids the need to create and manage a Lambda function or a Glue crawler, which are required for the other solutions.
The other solutions have the following drawbacks:
Using AWS Glue to export the data from DynamoDB to S3 introduces additional latency and complexity, as Glue is a batch-oriented service that requires scheduling and configuration3. Using an API call from QuickSight to access the data in DynamoDB directly is not possible, as QuickSight does not support direct querying of DynamoDB4.
Using Kinesis Data Firehose to export the data from DynamoDB to S3 is less efficient and flexible than using Kinesis Data Streams, as Firehose does not support custom data processing or transformation, and has a minimum buffer interval of 60 seconds5.
References:
1: Amazon Kinesis Data Streams – Amazon Web Services
2: Visualize Amazon DynamoDB insights in Amazon QuickSight using the Amazon Athena DynamoDB connector and AWS Glue | AWS Big Data Blog
3: AWS Glue – Amazon Web Services
4: Visualising your Amazon DynamoDB data with Amazon QuickSight – DEV Community
5: Amazon Kinesis Data Firehose – Amazon Web Services