AWS Certified Machine Learning Engineer - Associate MLA-C01Free trialFree trial

By amazon
Aug, 2025

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Question 1

Case Study -
A company is building a web-based AI application by using Amazon SageMaker. The application will provide the following capabilities and features: ML experimentation, training, a central model registry, model deployment, and model monitoring.
The application must ensure secure and isolated use of training data during the ML lifecycle. The training data is stored in Amazon S3.
The company needs to use the central model registry to manage different versions of models in the application.
Which action will meet this requirement with the LEAST operational overhead?

  • A: Create a separate Amazon Elastic Container Registry (Amazon ECR) repository for each model.
  • B: Use Amazon Elastic Container Registry (Amazon ECR) and unique tags for each model version.
  • C: Use the SageMaker Model Registry and model groups to catalog the models.
  • D: Use the SageMaker Model Registry and unique tags for each model version.
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Question 2

Case study -
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
Which AWS service or feature can aggregate the data from the various data sources?

  • A: Amazon EMR Spark jobs
  • B: Amazon Kinesis Data Streams
  • C: Amazon DynamoDB
  • D: AWS Lake Formation
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Question 3

A company is using Amazon SageMaker to develop ML models. The company stores sensitive training data in an Amazon S3 bucket. The model training must have network isolation from the internet.

Which solution will meet this requirement?

  • A: Run the SageMaker training jobs in private subnets. Create a NAT gateway. Route traffic for training through the NAT gateway.
  • B: Run the SageMaker training jobs in private subnets. Create an S3 gateway VPC endpoint. Route traffic for training through the S3 gateway VPC endpoint.
  • C: Run the SageMaker training jobs in public subnets that have an attached security group. In the security group, use inbound rules to limit traffic from the internet. Encrypt SageMaker instance storage by using server-side encryption with AWS KMS keys (SSE-KMS).
  • D: Encrypt traffic to Amazon S3 by using a bucket policy that includes a value of True for the aws:SecureTransport condition key. Use default at-rest encryption for Amazon S3. Encrypt SageMaker instance storage by using server-side encryption with AWS KMS keys (SSE-KMS).
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Question 4

A company needs an AWS solution that will automatically create versions of ML models as the models are created.

Which solution will meet this requirement?

  • A: Amazon Elastic Container Registry (Amazon ECR)
  • B: Model packages from Amazon SageMaker Marketplace
  • C: Amazon SageMaker ML Lineage Tracking
  • D: Amazon SageMaker Model Registry
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Question 5

A company needs to use Retrieval Augmented Generation (RAG) to supplement an open source large language model (LLM) that runs on Amazon Bedrock. The company's data for RAG is a set of documents in an Amazon S3 bucket. The documents consist of .csv files and .docx files.

Which solution will meet these requirements with the LEAST operational overhead?

  • A: Create a pipeline in Amazon SageMaker Pipelines to generate a new model. Call the new model from Amazon Bedrock to perform RAG queries.
  • B: Convert the data into vectors. Store the data in an Amazon Neptune database. Connect the database to Amazon Bedrock. Call the Amazon Bedrock API to perform RAG queries.
  • C: Fine-tune an existing LLM by using an AutoML job in Amazon SageMaker. Configure the S3 bucket as a data source for the AutoML job. Deploy the LLM to a SageMaker endpoint. Use the endpoint to perform RAG queries.
  • D: Create a knowledge base for Amazon Bedrock. Configure a data source that references the S3 bucket. Use the Amazon Bedrock API to perform RAG queries.
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Question 6

A company plans to deploy an ML model for production inference on an Amazon SageMaker endpoint. The average inference payload size will vary from 100 MB to 300 MB. Inference requests must be processed in 60 minutes or less.

Which SageMaker inference option will meet these requirements?

  • A: Serverless inference
  • B: Asynchronous inference
  • C: Real-time inference
  • D: Batch transform
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Question 7

An ML engineer notices class imbalance in an image classification training job.

What should the ML engineer do to resolve this issue?

  • A: Reduce the size of the dataset.
  • B: Transform some of the images in the dataset.
  • C: Apply random oversampling on the dataset.
  • D: Apply random data splitting on the dataset.
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Question 8

A company receives daily .csv files about customer interactions with its ML model. The company stores the files in Amazon S3 and uses the files to retrain the model. An ML engineer needs to implement a solution to mask credit card numbers in the files before the model is retrained.

Which solution will meet this requirement with the LEAST development effort?

  • A: Create a discovery job in Amazon Macie. Configure the job to find and mask sensitive data.
  • B: Create Apache Spark code to run on an AWS Glue job. Use the Sensitive Data Detection functionality in AWS Glue to find and mask sensitive data.
  • C: Create Apache Spark code to run on an AWS Glue job. Program the code to perform a regex operation to find and mask sensitive data.
  • D: Create Apache Spark code to run on an Amazon EC2 instance. Program the code to perform an operation to find and mask sensitive data.
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Question 9

A medical company is using AWS to build a tool to recommend treatments for patients. The company has obtained health records and self-reported textual information in English from patients. The company needs to use this information to gain insight about the patients.

Which solution will meet this requirement with the LEAST development effort?

  • A: Use Amazon SageMaker to build a recurrent neural network (RNN) to summarize the data.
  • B: Use Amazon Comprehend Medical to summarize the data.
  • C: Use Amazon Kendra to create a quick-search tool to query the data.
  • D: Use the Amazon SageMaker Sequence-to-Sequence (seq2seq) algorithm to create a text summary from the data.
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Question 10

A company needs to extract entities from a PDF document to build a classifier model.

Which solution will extract and store the entities in the LEAST amount of time?

  • A: Use Amazon Comprehend to extract the entities. Store the output in Amazon S3.
  • B: Use an open source AI optical character recognition (OCR) tool on Amazon SageMaker to extract the entities. Store the output in Amazon S3.
  • C: Use Amazon Textract to extract the entities. Use Amazon Comprehend to convert the entities to text. Store the output in Amazon S3.
  • D: Use Amazon Textract integrated with Amazon Augmented AI (Amazon A2I) to extract the entities. Store the output in Amazon S3.
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Question 11

A company shares Amazon SageMaker Studio notebooks that are accessible through a VPN. The company must enforce access controls to prevent malicious actors from exploiting presigned URLs to access the notebooks.

Which solution will meet these requirements?

  • A: Set up Studio client IP validation by using the aws:sourceIp IAM policy condition.
  • B: Set up Studio client VPC validation by using the aws:sourceVpc IAM policy condition.
  • C: Set up Studio client role endpoint validation by using the aws:PrimaryTag IAM policy condition.
  • D: Set up Studio client user endpoint validation by using the aws:PrincipalTag IAM policy condition.
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Question 12

An ML engineer needs to merge and transform data from two sources to retrain an existing ML model. One data source consists of .csv files that are stored in an Amazon S3 bucket. Each .csv file consists of millions of records. The other data source is an Amazon Aurora DB cluster.

The result of the merge process must be written to a second S3 bucket. The ML engineer needs to perform this merge-and-transform task every week.

Which solution will meet these requirements with the LEAST operational overhead?

  • A: Create a transient Amazon EMR cluster every week. Use the cluster to run an Apache Spark job to merge and transform the data.
  • B: Create a weekly AWS Glue job that uses the Apache Spark engine. Use DynamicFrame native operations to merge and transform the data.
  • C: Create an AWS Lambda function that runs Apache Spark code every week to merge and transform the data. Configure the Lambda function to connect to the initial S3 bucket and the DB cluster.
  • D: Create an AWS Batch job that runs Apache Spark code on Amazon EC2 instances every week. Configure the Spark code to save the data from the EC2 instances to the second S3 bucket.
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Question 13

Case study -
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
After the data is aggregated, the ML engineer must implement a solution to automatically detect anomalies in the data and to visualize the result.
Which solution will meet these requirements?

  • A: Use Amazon Athena to automatically detect the anomalies and to visualize the result.
  • B: Use Amazon Redshift Spectrum to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.
  • C: Use Amazon SageMaker Data Wrangler to automatically detect the anomalies and to visualize the result.
  • D: Use AWS Batch to automatically detect the anomalies. Use Amazon QuickSight to visualize the result.
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Question 14

An ML engineer has deployed an Amazon SageMaker model to a serverless endpoint in production. The model is invoked by the InvokeEndpoint API operation.

The model's latency in production is higher than the baseline latency in the test environment. The ML engineer thinks that the increase in latency is because of model startup time.

What should the ML engineer do to confirm or deny this hypothesis?

  • A: Schedule a SageMaker Model Monitor job. Observe metrics about model quality.
  • B: Schedule a SageMaker Model Monitor job with Amazon CloudWatch metrics enabled.
  • C: Enable Amazon CloudWatch metrics. Observe the ModelSetupTime metric in the SageMaker namespace.
  • D: Enable Amazon CloudWatch metrics. Observe the ModelLoadingWaitTime metric in the SageMaker namespace.
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Question 15

An ML engineer needs to ensure that a dataset complies with regulations for personally identifiable information (PII). The ML engineer will use the data to train an ML model on Amazon SageMaker instances. SageMaker must not use any of the PII.

Which solution will meet these requirements in the MOST operationally efficient way?

  • A: Use the Amazon Comprehend DetectPiiEntities API call to redact the PII from the data. Store the data in an Amazon S3 bucket. Access the S3 bucket from the SageMaker instances for model training.
  • B: Use the Amazon Comprehend DetectPiiEntities API call to redact the PII from the data. Store the data in an Amazon Elastic File System (Amazon EFS) file system. Mount the EFS file system to the SageMaker instances for model training.
  • C: Use AWS Glue DataBrew to cleanse the dataset of PII. Store the data in an Amazon Elastic File System (Amazon EFS) file system. Mount the EFS file system to the SageMaker instances for model training.
  • D: Use Amazon Macie for automatic discovery of PII in the data. Remove the PII. Store the data in an Amazon S3 bucket. Mount the S3 bucket to the SageMaker instances for model training.
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Question 16

A company must install a custom script on any newly created Amazon SageMaker notebook instances.

Which solution will meet this requirement with the LEAST operational overhead?

  • A: Create a lifecycle configuration script to install the custom script when a new SageMaker notebook is created. Attach the lifecycle configuration to every new SageMaker notebook as part of the creation steps.
  • B: Create a custom Amazon Elastic Container Registry (Amazon ECR) image that contains the custom script. Push the ECR image to a Docker registry. Attach the Docker image to a SageMaker Studio domain. Select the kernel to run as part of the SageMaker notebook.
  • C: Create a custom package index repository. Use AWS CodeArtifact to manage the installation of the custom script. Set up AWS PrivateLink endpoints to connect CodeArtifact to the SageMaker instance. Install the script.
  • D: Store the custom script in Amazon S3. Create an AWS Lambda function to install the custom script on new SageMaker notebooks. Configure Amazon EventBridge to invoke the Lambda function when a new SageMaker notebook is initialized.
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Question 17

A company is building a real-time data processing pipeline for an ecommerce application. The application generates a high volume of clickstream data that must be ingested, processed, and visualized in near real time. The company needs a solution that supports SQL for data processing and Jupyter notebooks for interactive analysis.

Which solution will meet these requirements?

  • A: Use Amazon Data Firehose to ingest the data. Create an AWS Lambda function to process the data. Store the processed data in Amazon S3. Use Amazon QuickSight to visualize the data.
  • B: Use Amazon Kinesis Data Streams to ingest the data. Use Amazon Data Firehose to transform the data. Use Amazon Athena to process the data. Use Amazon QuickSight to visualize the data.
  • C: Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data. Use AWS Glue with PySpark to process the data. Store the processed data in Amazon S3. Use Amazon QuickSight to visualize the data.
  • D: Use Amazon Managed Streaming for Apache Kafka (Amazon MSK) to ingest the data. Use Amazon Managed Service for Apache Flink to process the data. Use the built-in Flink dashboard to visualize the data.
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Question 18

A medical company needs to store clinical data. The data includes personally identifiable information (PII) and protected health information (PHI).

An ML engineer needs to implement a solution to ensure that the PII and PHI are not used to train ML models.

Which solution will meet these requirements?

  • A: Store the clinical data in Amazon S3 buckets. Use AWS Glue DataBrew to mask the PII and PHI before the data is used for model training.
  • B: Upload the clinical data to an Amazon Redshift database. Use built-in SQL stored procedures to automatically classify and mask the PII and PHI before the data is used for model training.
  • C: Use Amazon Comprehend to detect and mask the PII before the data is used for model training. Use Amazon Comprehend Medical to detect and mask the PHI before the data is used for model training.
  • D: Create an AWS Lambda function to encrypt the PII and PHI. Program the Lambda function to save the encrypted data to an Amazon S3 bucket for model training.
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Question 19

Case study -
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
The training dataset includes categorical data and numerical data. The ML engineer must prepare the training dataset to maximize the accuracy of the model.
Which action will meet this requirement with the LEAST operational overhead?

  • A: Use AWS Glue to transform the categorical data into numerical data.
  • B: Use AWS Glue to transform the numerical data into categorical data.
  • C: Use Amazon SageMaker Data Wrangler to transform the categorical data into numerical data.
  • D: Use Amazon SageMaker Data Wrangler to transform the numerical data into categorical data.
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Question 20

Case study -
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
Before the ML engineer trains the model, the ML engineer must resolve the issue of the imbalanced data.
Which solution will meet this requirement with the LEAST operational effort?

  • A: Use Amazon Athena to identify patterns that contribute to the imbalance. Adjust the dataset accordingly.
  • B: Use Amazon SageMaker Studio Classic built-in algorithms to process the imbalanced dataset.
  • C: Use AWS Glue DataBrew built-in features to oversample the minority class.
  • D: Use the Amazon SageMaker Data Wrangler balance data operation to oversample the minority class.
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Question 21

Case study -
An ML engineer is developing a fraud detection model on AWS. The training dataset includes transaction logs, customer profiles, and tables from an on-premises MySQL database. The transaction logs and customer profiles are stored in Amazon S3.
The dataset has a class imbalance that affects the learning of the model's algorithm. Additionally, many of the features have interdependencies. The algorithm is not capturing all the desired underlying patterns in the data.
The ML engineer needs to use an Amazon SageMaker built-in algorithm to train the model.
Which algorithm should the ML engineer use to meet this requirement?

  • A: LightGBM
  • B: Linear learner
  • C: К-means clustering
  • D: Neural Topic Model (NTM)
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Question 22

A company has deployed an XGBoost prediction model in production to predict if a customer is likely to cancel a subscription. The company uses Amazon SageMaker Model Monitor to detect deviations in the F1 score.
During a baseline analysis of model quality, the company recorded a threshold for the F1 score. After several months of no change, the model's F1 score decreases significantly.
What could be the reason for the reduced F1 score?

  • A: Concept drift occurred in the underlying customer data that was used for predictions.
  • B: The model was not sufficiently complex to capture all the patterns in the original baseline data.
  • C: The original baseline data had a data quality issue of missing values.
  • D: Incorrect ground truth labels were provided to Model Monitor during the calculation of the baseline.
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Question 23

A company has a team of data scientists who use Amazon SageMaker notebook instances to test ML models. When the data scientists need new permissions, the company attaches the permissions to each individual role that was created during the creation of the SageMaker notebook instance.
The company needs to centralize management of the team's permissions.
Which solution will meet this requirement?

  • A: Create a single IAM role that has the necessary permissions. Attach the role to each notebook instance that the team uses.
  • B: Create a single IAM group. Add the data scientists to the group. Associate the group with each notebook instance that the team uses.
  • C: Create a single IAM user. Attach the AdministratorAccess AWS managed IAM policy to the user. Configure each notebook instance to use the IAM user.
  • D: Create a single IAM group. Add the data scientists to the group. Create an IAM role. Attach the AdministratorAccess AWS managed IAM policy to the role. Associate the role with the group. Associate the group with each notebook instance that the team uses.
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