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Free AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam Braindumps
AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions 105–112 (page: 15)

Updated On: 15-Apr-2026
Page 15 of 33
PDF with 270 Questions

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?

  1. Create a discovery job in Amazon Macie. Configure the job to find and mask sensitive data.
  2. 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.
  3. 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.
  4. Create Apache Spark code to run on an Amazon EC2 instance. Program the code to perform an operation to find and mask sensitive data.

Answer(s): B

Explanation:

AWS Glue provides built-in Sensitive Data Detection functionality, which can automatically identify and mask sensitive information such as credit card numbers in .csv files stored in Amazon S3. This solution requires minimal development effort while integrating seamlessly with the company's existing AWS-based ML pipeline.
AWS Glue Sensitive Data Detection can automatically detect credit card numbers without requiring manual



regex pattern implementation.
Glue jobs are serverless, reducing operational overhead compared to managing EC2 instances.

Built-in masking capabilities make it easier to sanitize data before retraining the model.



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?

  1. Use Amazon SageMaker to build a recurrent neural network (RNN) to summarize the data.
  2. Use Amazon Comprehend Medical to summarize the data.
  3. Use Amazon Kendra to create a quick-search tool to query the data.
  4. Use the Amazon SageMaker Sequence-to-Sequence (seq2seq) algorithm to create a text summary from the data.

Answer(s): B

Explanation:

Amazon Comprehend Medical is a fully managed natural language processing (NLP) service that is specifically designed to analyze medical texts with minimal development effort. It can:
Extract key medical information such as conditions, medications, treatments, and symptoms from unstructured text.
Summarize health records efficiently without requiring custom ML model development.

Reduce regulatory and compliance burden by providing HIPAA-eligible services.



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?

  1. Use Amazon Comprehend to extract the entities. Store the output in Amazon S3.
  2. Use an open source AI optical character recognition (OCR) tool on Amazon SageMaker to extract the entities. Store the output in Amazon S3.
  3. Use Amazon Textract to extract the entities. Use Amazon Comprehend to convert the entities to text. Store the output in Amazon S3.
  4. Use Amazon Textract integrated with Amazon Augmented AI (Amazon A2I) to extract the entities. Store the output in Amazon S3.

Answer(s): C

Explanation:

To extract entities from a PDF document quickly and efficiently, the best approach is:
Amazon Textract: Extracts text from the PDF, including structured data such as tables and forms.

Amazon Comprehend: Extracts entities from the text, such as names, dates, locations, and custom entity types.
Amazon S3: Stores the extracted data for further processing.



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?

  1. Set up Studio client IP validation by using the aws:sourceIp IAM policy condition.
  2. Set up Studio client VPC validation by using the aws:sourceVpc IAM policy condition.
  3. Set up Studio client role endpoint validation by using the aws:PrimaryTag IAM policy condition.
  4. Set up Studio client user endpoint validation by using the aws:PrincipalTag IAM policy condition.

Answer(s): A

Explanation:

To prevent malicious actors from exploiting presigned URLs to access SageMaker Studio notebooks, the best approach is to restrict access based on IP addresses using the aws:sourceIp IAM policy condition.
SageMaker Studio generates presigned URLs that allow access to notebooks. If not properly restricted, these URLs could be used outside of the intended network.
By enforcing IP-based restrictions (aws:sourceIp), access is limited to only authorized users on the VPN, ensuring security.



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?

  1. Create a transient Amazon EMR cluster every week. Use the cluster to run an Apache Spark job to merge and transform the data.
  2. Create a weekly AWS Glue job that uses the Apache Spark engine. Use DynamicFrame native operations to merge and transform the data.
  3. 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.
  4. 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.

Answer(s): B

Explanation:

AWS Glue is a fully managed ETL (Extract, Transform, Load) service that provides serverless data transformation capabilities using Apache Spark. Using AWS Glue with DynamicFrames allows the ML engineer to efficiently merge and transform large datasets with minimal operational overhead.
AWS Glue is serverless, eliminating the need for provisioning and managing infrastructure.
Glue jobs can be scheduled weekly to automate the merge-and-transform process.
DynamicFrames provide built-in transformations optimized for merging data from multiple sources, including S3 and Aurora.
Apache Spark in Glue ensures scalability when processing millions of records efficiently.



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?

  1. Schedule a SageMaker Model Monitor job. Observe metrics about model quality.
  2. Schedule a SageMaker Model Monitor job with Amazon CloudWatch metrics enabled.
  3. Enable Amazon CloudWatch metrics. Observe the ModelSetupTime metric in the SageMaker namespace.
  4. Enable Amazon CloudWatch metrics. Observe the ModelLoadingWaitTime metric in the SageMaker namespace.

Answer(s): D

Explanation:

In Amazon SageMaker Serverless Inference, cold starts can cause increased latency when a model is invoked after being inactive for a period. The ModelLoadingWaitTime CloudWatch metric in the SageMaker namespace measures the time taken to load the model into memory before inference, helping to determine if startup time is the cause of latency.
If ModelLoadingWaitTime is high, it confirms that the latency is due to model startup time (cold starts).
If ModelLoadingWaitTime is low, the latency is likely caused by other factors (e.g., model complexity or instance type).



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?

  1. 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.
  2. 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.
  3. 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.
  4. 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.

Answer(s): A

Explanation:

The most operationally efficient way to ensure compliance with PII regulations is to:
1. Use Amazon Comprehend DetectPiiEntities to automatically detect and redact PII from the dataset.
2. Store the sanitized data in Amazon S3, which is scalable, cost-effective, and natively integrates with SageMaker training jobs.
3. Access the S3 bucket directly from SageMaker instances, ensuring that only non-PII data is used in training.



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?

  1. 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.
  2. 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.
  3. 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.
  4. 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.

Answer(s): A

Explanation:

The simplest and most operationally efficient way to automatically install a custom script on new SageMaker notebook instances is by using a lifecycle configuration script.
Lifecycle configurations allow you to run custom shell scripts at the start of every new notebook instance. This ensures the custom script is installed consistently and automatically without requiring manual intervention.
Attaching the lifecycle configuration to new SageMaker notebooks ensures that the installation happens as part of the notebook creation process.



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