Free Amazon MLS-C01 Practice Exams

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 #251

A financial services company is building a robust serverless data lake on Amazon S3.

The data lake should be flexible and meet the following requirements:

* Support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum.

* Support event-driven ETL pipelines.

* Provide a quick and easy way to understand metadata.

Which approach meets trfese requirements?

  • A . Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.
  • B . Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.
  • C . Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
  • D . Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.

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Correct Answer: A
A

Explanation:

To build a robust serverless data lake on Amazon S3 that meets the requirements, the financial services company should use the following AWS services:

AWS Glue crawler: This is a service that connects to a data store, progresses through a prioritized list of classifiers to determine the schema for the data, and then creates metadata tables in the AWS Glue Data Catalog1. The company can use an AWS Glue crawler to crawl the S3 data and infer the schema, format, and partition structure of the data. The crawler can also detect schema changes and update the metadata tables accordingly. This enables the company to support querying old and new data on Amazon S3 through Amazon Athena and Amazon Redshift Spectrum, which are serverless interactive query services that use the AWS Glue Data Catalog as a central location for storing and retrieving table metadata23.

AWS Lambda function: This is a service that lets you run code without provisioning or managing servers. You pay only for the compute time you consume – there is no charge when your code is not running. You can also use AWS Lambda to create event-driven ETL pipelines, by triggering other AWS services based on events such as object creation or deletion in S3 buckets4. The company can use an AWS Lambda function to trigger an AWS Glue ETL job, which is a serverless way to extract, transform, and load data for analytics. The AWS Glue ETL job can perform various data processing tasks, such as converting data formats, filtering, aggregating, joining, and more.

AWS Glue Data Catalog: This is a managed service that acts as a central metadata repository for data assets across AWS and on-premises data sources. The AWS Glue Data Catalog provides a uniform repository where disparate systems can store and find metadata to keep track of data in data silos, and use that metadata to query and transform the data. The company can use the AWS Glue Data Catalog to search and discover metadata, such as table definitions, schemas, and partitions. The AWS Glue Data Catalog also integrates with Amazon Athena, Amazon Redshift Spectrum, Amazon EMR, and AWS Glue ETL jobs, providing a consistent view of the data across different query and analysis services.

References:

1: What Is a Crawler? – AWS Glue

2: What Is Amazon Athena? – Amazon Athena

3: Amazon Redshift Spectrum – Amazon Redshift

4: What is AWS Lambda? – AWS Lambda

: AWS Glue ETL Jobs – AWS Glue

: What Is the AWS Glue Data Catalog? – AWS Glue
Question #252

IT leadership wants Jo transition a company’s existing machine learning data storage environment to

AWS as a temporary ad hoc solution. The company currently uses a custom software process that heavily leverages SOL as a query language and exclusively stores generated csv documents for machine learning

The ideal state for the company would be a solution that allows it to continue to use the current workforce of SQL experts. The solution must also support the storage of csv and JSON files, and be able to query over semi-structured data.

The following are high priorities for the company:

• Solution simplicity

• Fast development time

• Low cost

• High flexibility

What technologies meet the company’s requirements?

  • A . Amazon S3 and Amazon Athena
  • B . Amazon Redshift and AWS Glue
  • C . Amazon DynamoDB and DynamoDB Accelerator (DAX)
  • D . Amazon RDS and Amazon ES

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Correct Answer: A
A

Explanation:

Amazon S3 and Amazon Athena are technologies that meet the company’s requirements for a temporary ad hoc solution for machine learning data storage and query.

Amazon S3 and Amazon Athena have the following features and benefits:

Amazon S3 is a service that provides scalable, durable, and secure object storage for any type of data. Amazon S3 can store csv and JSON files, as well as other formats, and can handle large volumes of data with high availability and performance. Amazon S3 also integrates with other AWS services, such as Amazon Athena, for further processing and analysis of the data.

Amazon Athena is a service that allows querying data stored in Amazon S3 using standard SQL. Amazon Athena can query over semi-structured data, such as JSON, as well as structured data, such as csv, without requiring any loading or transformation. Amazon Athena is serverless, meaning that there is no infrastructure to manage and users only pay for the queries they run. Amazon Athena also supports the use of AWS Glue Data Catalog, which is a centralized metadata repository that can store and manage the schema and partition information of the data in Amazon S3.

Using Amazon S3 and Amazon Athena, the company can achieve the following high priorities: Solution simplicity: Amazon S3 and Amazon Athena are easy to use and require minimal configuration and maintenance. The company can simply upload the csv and JSON files to Amazon S3 and use Amazon Athena to query them using SQL. The company does not need to worry about provisioning, scaling, or managing any servers or clusters.

Fast development time: Amazon S3 and Amazon Athena can enable the company to quickly access and analyze the data without any data preparation or loading. The company can use the existing workforce of SQL experts to write and run queries on Amazon Athena and get results in seconds or minutes.

Low cost: Amazon S3 and Amazon Athena are cost-effective and offer pay-as-you-go pricing models. Amazon S3 charges based on the amount of storage used and the number of requests made. Amazon Athena charges based on the amount of data scanned by the queries. The company can also reduce the costs by using compression, encryption, and partitioning techniques to optimize the data storage and query performance.

High flexibility: Amazon S3 and Amazon Athena are flexible and can support various data types,

formats, and sources. The company can store and query any type of data in Amazon S3, such as csv, JSON, Parquet, ORC, etc. The company can also query data from multiple sources in Amazon S3, such as data lakes, data warehouses, log files, etc.

The other options are not as suitable as option A for the company’s requirements for the following reasons:

Option B: Amazon Redshift and AWS Glue are technologies that can be used for data warehousing and data integration, but they are not ideal for a temporary ad hoc solution. Amazon Redshift is a service that provides a fully managed, petabyte-scale data warehouse that can run complex analytical queries using SQL. AWS Glue is a service that provides a fully managed extract, transform, and load (ETL) service that can prepare and load data for analytics. However, using Amazon Redshift and AWS Glue would require more effort and cost than using Amazon S3 and Amazon Athena. The company would need to load the data from Amazon S3 to Amazon Redshift using AWS Glue, which can take time and incur additional charges. The company would also need to manage the capacity and performance of the Amazon Redshift cluster, which can be complex and expensive.

Option C: Amazon DynamoDB and DynamoDB Accelerator (DAX) are technologies that can be used for fast and scalable NoSQL database and caching, but they are not suitable for the company’s data storage and query needs. Amazon DynamoDB is a service that provides a fully managed, key-value and document database that can deliver single-digit millisecond performance at any scale.

DynamoDB Accelerator (DAX) is a service that provides a fully managed, in-memory cache for DynamoDB that can improve the read performance by up to 10 times. However, using Amazon DynamoDB and DAX would not allow the company to continue to use SQL as a query language, as Amazon DynamoDB does not support SQL. The company would need to use the DynamoDB API or the AWS SDKs to access and query the data, which can require more coding and learning effort. The company would also need to transform the csv and JSON files into DynamoDB items, which can involve additional processing and complexity.

Option D: Amazon RDS and Amazon ES are technologies that can be used for relational database and search and analytics, but they are not optimal for the company’s data storage and query scenario. Amazon RDS is a service that provides a fully managed, relational database that supports various database engines, such as MySQL, PostgreSQL, Oracle, etc. Amazon ES is a service that provides a fully managed, Elasticsearch cluster, which is mainly used for search and analytics purposes. However, using Amazon RDS and Amazon ES would not be as simple and cost-effective as using Amazon S3 and Amazon Athena. The company would need to load the data from Amazon S3 to Amazon RDS, which can take time and incur additional charges. The company would also need to manage the capacity and performance of the Amazon RDS and Amazon ES clusters, which can be complex and expensive. Moreover, Amazon RDS and Amazon ES are not designed to handle semi-structured data, such as JSON, as well as Amazon S3 and Amazon Athena.

References:

Amazon S3

Amazon Athena

Amazon Redshift

AWS Glue

Amazon DynamoDB

[DynamoDB Accelerator (DAX)]

[Amazon RDS]

[Amazon ES]
Question #253

A machine learning (ML) specialist is training a linear regression model. The specialist notices that the model is overfitting. The specialist applies an L1 regularization parameter and runs the model again. This change results in all features having zero weights.

What should the ML specialist do to improve the model results?

  • A . Increase the L1 regularization parameter. Do not change any other training parameters.
  • B . Decrease the L1 regularization parameter. Do not change any other training parameters.
  • C . Introduce a large L2 regularization parameter. Do not change the current L1 regularization value.
  • D . Introduce a small L2 regularization parameter. Do not change the current L1 regularization value.

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Correct Answer: B
B

Explanation:

Applying L1 regularization encourages sparsity by penalizing weights directly, often driving many weights to zero. In this case, the ML specialist observes that all weights become zero, which suggests that the L1 regularization parameter is set too high. This high value overly penalizes non-zero weights, effectively removing all features from the model.

To improve the model, the ML specialist should reduce the L1 regularization parameter, allowing some features to retain non-zero weights. This adjustment will make the model less prone to excessive sparsity, allowing it to better capture essential patterns in the data without dropping all features. Introducing L2 regularization is another approach but may not directly resolve this specific issue of all-zero weights as effectively as reducing L1.
Question #254

A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet

of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance’s Amazon EBS volume, and needs to take a snapshot of that EBS volume. However the ML Specialist cannot find the Amazon SageMaker notebook instance’s EBS volume or Amazon EC2 instance within the VPC.

Why is the ML Specialist not seeing the instance visible in the VPC?

  • A . Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
  • B . Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.
  • C . Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
  • D . Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS Service accounts.

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Correct Answer: C
C

Explanation:

Amazon SageMaker notebook instances are fully managed environments that provide an integrated Jupyter notebook interface for data exploration, analysis, and machine learning. Amazon SageMaker notebook instances are based on EC2 instances that run within AWS service accounts, not within customer accounts. This means that the ML Specialist cannot find the Amazon SageMaker notebook instance’s EC2 instance or EBS volume within the VPC, as they are not visible or accessible to the customer. However, the ML Specialist can still take a snapshot of the EBS volume by using the Amazon SageMaker console or API. The ML Specialist can also use VPC interface endpoints to securely connect the Amazon SageMaker notebook instance to the resources within the VPC, such as Amazon S3 buckets, Amazon EFS file systems, or Amazon RDS databases
Question #255

A Machine Learning Specialist is attempting to build a linear regression model.

Given the displayed residual plot only, what is the MOST likely problem with the model?

  • A . Linear regression is inappropriate. The residuals do not have constant variance.
  • B . Linear regression is inappropriate. The underlying data has outliers.
  • C . Linear regression is appropriate. The residuals have a zero mean.
  • D . Linear regression is appropriate. The residuals have constant variance.

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Correct Answer: A
A

Explanation:

A residual plot is a type of plot that displays the values of a predictor variable in a regression model along the x-axis and the values of the residuals along the y-axis. This plot is used to assess whether or not the residuals in a regression model are normally distributed and whether or not they exhibit heteroscedasticity. Heteroscedasticity means that the variance of the residuals is not constant across different values of the predictor variable. This violates one of the assumptions of linear regression and can lead to biased estimates and unreliable predictions. The displayed residual plot shows a clear pattern of heteroscedasticity, as the residuals spread out as the fitted values increase. This indicates that linear regression is inappropriate for this data and a different model should be used.

References:

Regression – Amazon Machine Learning

How to Create a Residual Plot by Hand

How to Create a Residual Plot in Python
Question #256

A manufacturing company has a production line with sensors that collect hundreds of quality metrics. The company has stored sensor data and manual inspection results in a data lake for several months. To automate quality control, the machine learning team must build an automated mechanism that determines whether the produced goods are good quality, replacement market quality, or scrap quality based on the manual inspection results.

Which modeling approach will deliver the MOST accurate prediction of product quality?

  • A . Amazon SageMaker DeepAR forecasting algorithm
  • B . Amazon SageMaker XGBoost algorithm
  • C . Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm
  • D . A convolutional neural network (CNN) and ResNet

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Correct Answer: D
D

Explanation:

A convolutional neural network (CNN) is a type of deep learning model that can learn to extract features from images and perform tasks such as classification, segmentation, and detection1. ResNet is a popular CNN architecture that uses residual connections to overcome the problem of vanishing gradients and enable very deep networks2. For the task of predicting product quality based on sensor data, a CNN and ResNet approach can leverage the spatial structure of the data and learn complex patterns that distinguish different quality levels.

References:

Convolutional Neural Networks (CNNs / ConvNets)

PyTorch ResNet: The Basics and a Quick Tutorial
Question #257

A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker. The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm. The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model

What should the Specialist do to address the performance issues with the current solution?

  • A . Use the SageMaker batch transform feature
  • B . Compress the training data into Apache Parquet format.
  • C . Ensure that the input mode for the training job is set to Pipe.
  • D . Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.

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Correct Answer: C
C

Explanation:

The input mode for the training job determines how the training data is transferred from Amazon S3 to the SageMaker instance. There are two input modes: File and Pipe. File mode copies the entire training dataset from S3 to the local file system of the instance before starting the training job. This can cause a long delay before the training job launches, especially if the dataset is large. Pipe mode streams the data from S3 to the instance as the training job runs. This can reduce the startup time and improve the I/O throughput, as the data is read in smaller batches. Therefore, to address the performance issues with the current solution, the Specialist should ensure that the input mode for the training job is set to Pipe. This can be done by using the SageMaker Python SDK and setting the input_mode parameter to Pipe when creating the estimator or the fit method12. Alternatively, this can be done by using the AWS CLI and setting the InputMode parameter to Pipe when creating the training job3.

Reference: Access Training Data – Amazon SageMaker

Choosing Data Input Mode Using the SageMaker Python SDK – Amazon SageMaker CreateTrainingJob – Amazon SageMaker Service
Question #258

A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.

Which storage scheme is MOST adapted to this scenario?

  • A . Store datasets as files in Amazon S3.
  • B . Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
  • C . Store datasets as tables in a multi-node Amazon Redshift cluster.
  • D . Store datasets as global tables in Amazon DynamoDB.

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Correct Answer: A
A

Explanation:

The best storage scheme for this scenario is to store datasets as files in Amazon S3. Amazon S3 is a scalable, cost-effective, and durable object storage service that can store any amount and type of data. Amazon S3 also supports querying data using SQL with Amazon Athena, a serverless interactive query service that can analyze data directly in S3. This way, the Data Science team can easily explore and analyze their datasets without having to load them into a database or a compute instance.

The other options are not as suitable for this scenario because:

Storing datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance would limit the scalability and availability of the data, as EBS volumes are only accessible within a single availability zone and have a maximum size of 16 TiB. Also, EBS volumes are more expensive than S3 buckets and require provisioning and managing EC2 instances.

Storing datasets as tables in a multi-node Amazon Redshift cluster would incur higher costs and complexity than using S3 and Athena. Amazon Redshift is a data warehouse service that is optimized for analytical queries over structured or semi-structured data. However, it requires setting up and maintaining a cluster of nodes, loading data into tables, and choosing the right distribution and sort keys for optimal performance. Moreover, Amazon Redshift charges for both storage and compute, while S3 and Athena only charge for the amount of data stored and scanned, respectively.

Storing datasets as global tables in Amazon DynamoDB would not be feasible for large amounts of data, as DynamoDB is a key-value and document database service that is designed for fast and consistent performance at any scale. However, DynamoDB has a limit of 400 KB per item and 25 GB per partition key value, which may not be enough for storing large datasets. Also, DynamoDB does not support SQL queries natively, and would require using a service like Amazon EMR or AWS Glue to run SQL queries over DynamoDB data.

References:

Amazon S3 – Cloud Object Storage

Amazon Athena C Interactive SQL Queries for Data in Amazon S3 Amazon EBS – Amazon Elastic Block Store (EBS) Amazon Redshift C Data Warehouse Solution – AWS

Amazon DynamoDB C NoSQL Cloud Database Service
Question #259

A medical imaging company wants to train a computer vision model to detect areas of concern on patients’ CT scans. The company has a large collection of unlabeled CT scans that are linked to each patient and stored in an Amazon S3 bucket. The scans must be accessible to authorized users only. A machine learning engineer needs to build a labeling pipeline.

Which set of steps should the engineer take to build the labeling pipeline with the LEAST effort?

  • A . Create a workforce with AWS Identity and Access Management (IAM). Build a labeling tool on Amazon EC2 Queue images for labeling by using Amazon Simple Queue Service (Amazon SQS). Write the labeling instructions.
  • B . Create an Amazon Mechanical Turk workforce and manifest file. Create a labeling job by using the built-in image classification task type in Amazon SageMaker Ground Truth. Write the labeling instructions.
  • C . Create a private workforce and manifest file. Create a labeling job by using the built-in bounding box task type in Amazon SageMaker Ground Truth. Write the labeling instructions.
  • D . Create a workforce with Amazon Cognito. Build a labeling web application with AWS Amplify.
    Build a labeling workflow backend using AWS Lambda. Write the labeling instructions.

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Correct Answer: C
C

Explanation:

The engineer should create a private workforce and manifest file, and then create a labeling job by using the built-in bounding box task type in Amazon SageMaker Ground Truth. This will allow the engineer to build the labeling pipeline with the least effort.

A private workforce is a group of workers that you manage and who have access to your labeling tasks. You can use a private workforce to label sensitive data that requires confidentiality, such as medical images. You can create a private workforce by using Amazon Cognito and inviting workers by email. You can also use AWS Single Sign-On or your own authentication system to manage your private workforce.

A manifest file is a JSON file that lists the Amazon S3 locations of your input data. You can use a manifest file to specify the data objects that you want to label in your labeling job. You can create a manifest file by using the AWS CLI, the AWS SDK, or the Amazon SageMaker console.

A labeling job is a process that sends your input data to workers for labeling. You can use the Amazon SageMaker console to create a labeling job and choose from several built-in task types, such as image classification, text classification, semantic segmentation, and bounding box. A bounding box task type allows workers to draw boxes around objects in an image and assign labels to them. This is suitable for object detection tasks, such as identifying areas of concern on CT scans.

References:

Create and Manage Workforces – Amazon SageMaker

Use Input and Output Data – Amazon SageMaker

Create a Labeling Job – Amazon SageMaker

Bounding Box Task Type – Amazon SageMaker
Question #260

A company has video feeds and images of a subway train station. The company wants to create a deep learning model that will alert the station manager if any passenger crosses the yellow safety line when there is no train in the station. The alert will be based on the video feeds. The company wants the model to detect the yellow line, the passengers who cross the yellow line, and the trains in the video feeds. This task requires labeling. The video data must remain confidential.

A data scientist creates a bounding box to label the sample data and uses an object detection model. However, the object detection model cannot clearly demarcate the yellow line, the passengers who cross the yellow line, and the trains.

Which labeling approach will help the company improve this model?

  • A . Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a private workforce. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.
  • B . Use an Amazon SageMaker Ground Truth object detection labeling task. Use Amazon Mechanical Turk as the labeling workforce.
  • C . Use Amazon Rekognition Custom Labels to label the dataset and create a custom Amazon Rekognition object detection model. Create a workforce with a third-party AWS Marketplace vendor. Use Amazon Augmented AI (Amazon A2I) to review the low-confidence predictions and retrain the custom Amazon Rekognition model.
  • D . Use an Amazon SageMaker Ground Truth semantic segmentation labeling task. Use a private workforce as the labeling workforce.

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Correct Answer: D
D

Explanation:
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