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Free AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam Braindumps
AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions 113–120 (page: 16)

Updated On: 15-Apr-2026
Page 16 of 33
PDF with 270 Questions

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?

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

Answer(s): D

Explanation:

The company requires a real-time data processing pipeline that supports SQL-based processing and Jupyter notebooks for interactive analysis.
Amazon MSK (Managed Streaming for Apache Kafka) efficiently ingests high-volume, real-time clickstream data.
Amazon Managed Service for Apache Flink provides SQL support for real-time data processing and integrates seamlessly with Kafka. Flink allows stateful stream processing with low latency and scalability.
Flink's built-in dashboard allows real-time visualization of streaming data without requiring additional BI tools.
Flink also supports Jupyter notebooks for interactive analysis.



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?

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

Answer(s): C

Explanation:

Amazon Comprehend and Comprehend Medical are fully managed NLP services that can automatically detect and mask sensitive data such as PII (e.g., names, addresses, social security numbers) and PHI (e.g., medical conditions, medications, and procedures).
Amazon Comprehend: Detects and masks general PII before training.



Amazon Comprehend Medical: Identifies and masks PHI in clinical datasets, ensuring compliance with



healthcare regulations (e.g., HIPAA).
Ensures no sensitive data is included in model training while minimizing operational effort.



An ML engineer is developing a classification model. The ML engineer needs to use custom libraries in processing jobs, training jobs, and pipelines in Amazon SageMaker.

Which solution will provide this functionality with the LEAST implementation effort?

  1. Manually install the libraries in the SageMaker containers.
  2. Build a custom Docker container that includes the required libraries. Host the container in Amazon Elastic Container Registry (Amazon ECR). Use the ECR image in the SageMaker jobs and pipelines.
  3. Create a SageMaker notebook instance to host the jobs. Create an AWS Lambda function to install the libraries on the notebook instance when the notebook instance starts. Configure the SageMaker jobs and pipelines to run on the notebook instance.
  4. Run code for the libraries externally on Amazon EC2 instances. Store the results in Amazon S3. Import the results into the SageMaker jobs and pipelines.

Answer(s): B

Explanation:

Building a custom Docker container with the required libraries and hosting it in Amazon ECR allows SageMaker jobs, training, and pipelines to consistently use the same environment. This approach minimizes manual setup, ensures portability, and provides the least ongoing implementation effort compared to repeatedly installing or managing libraries separately.



An ML engineer is deploying a trained model to an Amazon SageMaker endpoint. The ML engineer needs to receive alerts when data quality issues occur in production.

Which solution will meet this requirement?

  1. Configure an Amazon CloudWatch metric alarm and a corresponding action to send an Amazon Simple Notification Service (Amazon SNS) notification.
  2. Integrate the SageMaker endpoint with a SageMaker Clarify processing job. Configure an Amazon CloudWatch alarm to provide alerts.
  3. Configure a monitoring job in SageMaker Model Monitor. Integrate Model Monitor with Amazon CloudWatch to provide alerts.
  4. Configure a data flow in SageMaker Data Wrangler. Integrate Data Wrangler with Amazon CloudWatch to provide alerts.

Answer(s): C

Explanation:

SageMaker Model Monitor is designed to continuously monitor deployed models for data quality issues such as data drift or violations of data constraints. By integrating Model Monitor with Amazon CloudWatch, alerts can be automatically triggered when data quality issues occur in production.



A company needs to use Amazon SageMaker to train a model on more than 300 GB of data. The training data is composed of files that are 200 MB in size. The data is stored in Amazon S3 Standard storage and feeds a dashboard tool.

Which SageMaker training ingestion mechanism is the MOST cost-effective solution for this scenario?

  1. Amazon Elastic File System (Amazon EFS) file system
  2. Amazon FSx for Lustre file system
  3. Amazon S3 in fast file mode while using S3 Express One Zone
  4. Amazon S3 in fast file mode without using S3 Express One Zone

Answer(s): D

Explanation:

For large-scale training data already stored in Amazon S3, the most cost-effective solution is to use SageMaker's S3 fast file mode without S3 Express One Zone. Fast file mode enables streaming directly from S3 without duplicating the dataset onto local storage, reducing startup time and storage cost. Using S3 Express One Zone would increase cost, so standard fast file mode is the most economical choice.



A company has an ML model that is deployed to an Amazon SageMaker endpoint for real-time inference. The company needs to deploy a new model. The company must compare the new model's performance to the currently deployed model's performance before shifting all traffic to the new model.

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

  1. Deploy the new model to a separate endpoint. Manually split traffic between the two endpoints.
  2. Deploy the new model to a separate endpoint. Use Amazon CloudFront to distribute traffic between the two endpoints.
  3. Deploy the new model as a shadow variant on the same endpoint as the current model. Route a portion of live traffic to the shadow model for evaluation.
  4. Use AWS Lambda functions with custom logic to route traffic between the current model and the new model.

Answer(s): C

Explanation:

SageMaker supports shadow variant deployments, which allow a new model to run alongside the current one on the same endpoint. A portion of live traffic is mirrored to the shadow model for evaluation, while only the current model's output is returned to users. This provides the required comparison with minimal operational effort, avoiding the need for custom traffic-splitting solutions.



A company runs an ML model on Amazon SageMaker. The company uses an automatic process that makes API calls to create training jobs for the model. The company has new compliance rules that prohibit the collection of aggregated metadata from training jobs.

Which solution will prevent SageMaker from collecting metadata from the training jobs?

  1. Opt out of metadata tracking for any training job that is submitted.
  2. Ensure that training jobs are running in a private subnet in a custom VPC.
  3. Encrypt the training data with an AWS Key Management Service (AWS KMS) customer managed key.
  4. Reconfigure the training jobs to use only AWS Nitro instances.

Answer(s): A

Explanation:

Amazon SageMaker automatically collects training job metadata, but you can opt out of metadata tracking when submitting a training job. This disables collection of aggregated metadata, ensuring compliance with rules that prohibit metadata collection.



A company is exploring generative AI and wants to add a new product feature. An ML engineer is making API calls from existing Amazon EC2 instances to Amazon Bedrock. The EC2 instances are in a private subnet and must remain private during the implementation. The EC2 instances have an assigned security group that allows access to all IP addresses in the private subnet.

What should the ML engineer do to establish a connection between the EC2 instances and Amazon Bedrock?

  1. Modify the security group to allow inbound and outbound traffic to and from Amazon Bedrock.
  2. Use AWS PrivateLink to access Amazon Bedrock through an interface VPC endpoint.
  3. Configure Amazon Bedrock to use the private subnet where the EC2 instances are deployed.
  4. Link the existing VPC to Amazon Bedrock by using an AWS Direct Connect connection.

Answer(s): B

Explanation:

Since the EC2 instances are in a private subnet and must not have public internet access, the correct solution is to use AWS PrivateLink with an interface VPC endpoint for Amazon Bedrock. This allows private connectivity from the VPC to the Bedrock service without exposing traffic to the public internet.



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