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Free AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam Braindumps
AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions 65–72 (page: 10)

Updated On: 15-Apr-2026
Page 10 of 33
PDF with 270 Questions

A company is using an AWS Lambda function to monitor the metrics from an ML model. An ML engineer needs to implement a solution to send an email message when the metrics breach a threshold.

Which solution will meet this requirement?

  1. Log the metrics from the Lambda function to AWS CloudTrail. Configure a CloudTrail trail to send the email message.
  2. Log the metrics from the Lambda function to Amazon CloudFront. Configure an Amazon CloudWatch alarm to send the email message.
  3. Log the metrics from the Lambda function to Amazon CloudWatch. Configure a CloudWatch alarm to send the email message.
  4. Log the metrics from the Lambda function to Amazon CloudWatch. Configure an Amazon CloudFront rule to send the email message.

Answer(s): C

Explanation:

Logging the metrics to Amazon CloudWatch allows the metrics to be tracked and monitored effectively.
CloudWatch Alarms can be configured to trigger when metrics breach a predefined threshold.
The alarm can be set to notify through Amazon Simple Notification Service (SNS), which can send email messages to the configured recipients.
This is the standard and most efficient way to achieve the desired functionality.



A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks.

What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?

  1. Adjust the model's parameters and hyperparameters.
  2. Initiate a manual Model Monitor job that uses the most recent production data.
  3. Create a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.
  4. Include additional data in the existing training set for the model. Retrain and redeploy the model.

Answer(s): C

Explanation:

When Model Monitor identifies data quality issues, it might be due to a shift in the data distribution compared to the original baseline. By creating a new baseline using the most recent production data and updating Model Monitor to evaluate against this baseline, the ML engineer ensures that the monitoring is aligned with the current data patterns. This approach mitigates false positives and reflects the updated data characteristics without immediately retraining the model.



A company has an ML model that generates text descriptions based on images that customers upload to the company's website. The images can be up to 50 MB in total size.

An ML engineer decides to store the images in an Amazon S3 bucket. The ML engineer must implement a processing solution that can scale to accommodate changes in demand.

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

  1. Create an Amazon SageMaker batch transform job to process all the images in the S3 bucket.
  2. Create an Amazon SageMaker Asynchronous Inference endpoint and a scaling policy. Run a script to make an inference request for each image.
  3. Create an Amazon Elastic Kubernetes Service (Amazon EKS) cluster that uses Karpenter for auto scaling.
    Host the model on the EKS cluster. Run a script to make an inference request for each image.
  4. Create an AWS Batch job that uses an Amazon Elastic Container Service (Amazon ECS) cluster. Specify a list of images to process for each AWS Batch job.

Answer(s): B

Explanation:

SageMaker Asynchronous Inference is designed for processing large payloads, such as images up to 50 MB, and can handle requests that do not require an immediate response.

It scales automatically based on the demand, minimizing operational overhead while ensuring cost-efficiency.
A script can be used to send inference requests for each image, and the results can be retrieved asynchronously. This approach is ideal for accommodating varying levels of traffic with minimal manual intervention.



An ML engineer needs to use AWS services to identify and extract meaningful unique keywords from documents.

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

  1. Use the Natural Language Toolkit (NLTK) library on Amazon EC2 instances for text pre-processing. Use the Latent Dirichlet Allocation (LDA) algorithm to identify and extract relevant keywords.
  2. Use Amazon SageMaker AI and the BlazingText algorithm. Apply custom pre-processing steps for stemming and removal of stop words. Calculate term frequency-inverse document frequency (TF-IDF) scores to identify and extract relevant keywords.
  3. Store the documents in an Amazon S3 bucket. Create AWS Lambda functions to process the documents and to run Python scripts for stemming and removal of stop words. Use bigram and trigram techniques to identify and extract relevant keywords.
  4. Use Amazon Comprehend custom entity recognition and key phrase extraction to identify and extract relevant keywords.

Answer(s): D

Explanation:

Amazon Comprehend provides pre-built functionality for key phrase extraction and can identify meaningful keywords from documents with minimal setup or operational overhead. It eliminates the need for manual preprocessing, stemming, or stop-word removal and does not require custom model development or infrastructure management. This makes it the most efficient and low-maintenance solution for the task.



A company needs to give its ML engineers appropriate access to training data. The ML engineers must access training data from only their own business group. The ML engineers must not be allowed to access training data from other business groups.

The company uses a single AWS account and stores all the training data in Amazon S3 buckets. All ML model training occurs in Amazon SageMaker.

Which solution will provide the ML engineers with the appropriate access?

  1. Enable S3 bucket versioning.
  2. Configure S3 Object Lock settings for each user.
  3. Add cross-origin resource sharing (CORS) policies to the S3 buckets.
  4. Create IAM policies. Attach the policies to IAM users or IAM roles.

Answer(s): D

Explanation:

By creating IAM policies with specific permissions, you can restrict access to Amazon S3 buckets or objects based on the user's business group. These policies can be attached to IAM users or IAM roles associated with the ML engineers, ensuring that each engineer can only access training data belonging to their group. This approach is secure, scalable, and aligns with AWS best practices for access control.



A company needs to host a custom ML model to perform forecast analysis. The forecast analysis will occur with predictable and sustained load during the same 2-hour period every day.

Multiple invocations during the analysis period will require quick responses. The company needs AWS to manage the underlying infrastructure and any auto scaling activities.

Which solution will meet these requirements?

  1. Schedule an Amazon SageMaker batch transform job by using AWS Lambda.
  2. Configure an Auto Scaling group of Amazon EC2 instances to use scheduled scaling.
  3. Use Amazon SageMaker Serverless Inference with provisioned concurrency.
  4. Run the model on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster on Amazon EC2 with pod auto scaling.

Answer(s): C

Explanation:

SageMaker Serverless Inference is ideal for workloads with predictable, intermittent demand. By enabling provisioned concurrency, the model can handle multiple invocations quickly during the high-demand 2-hour period. AWS manages the underlying infrastructure and scaling, ensuring the solution meets performance requirements with minimal operational overhead. This approach is cost-effective since it scales down when not in use.



A company's ML engineer has deployed an ML model for sentiment analysis to an Amazon SageMaker endpoint. The ML engineer needs to explain to company stakeholders how the model makes predictions.

Which solution will provide an explanation for the model's predictions?

  1. Use SageMaker Model Monitor on the deployed model.
  2. Use SageMaker Clarify on the deployed model.
  3. Show the distribution of inferences from A/B testing in Amazon CloudWatch.
  4. Add a shadow endpoint. Analyze prediction differences on samples.

Answer(s): B

Explanation:

SageMaker Clarify is designed to provide explainability for ML models. It can analyze feature importance and explain how input features influence the model's predictions. By using Clarify with the deployed SageMaker model, the ML engineer can generate insights and present them to stakeholders to explain the sentiment analysis predictions effectively.



An ML engineer is using Amazon SageMaker to train a deep learning model that requires distributed training. After some training attempts, the ML engineer observes that the instances are not performing as expected. The ML engineer identifies communication overhead between the training instances.

What should the ML engineer do to MINIMIZE the communication overhead between the instances?

  1. Place the instances in the same VPC subnet. Store the data in a different AWS Region from where the instances are deployed.
  2. Place the instances in the same VPC subnet but in different Availability Zones. Store the data in a different AWS Region from where the instances are deployed.
  3. Place the instances in the same VPC subnet. Store the data in the same AWS Region and Availability Zone where the instances are deployed.
  4. Place the instances in the same VPC subnet. Store the data in the same AWS Region but in a different Availability Zone from where the instances are deployed.

Answer(s): C

Explanation:

To minimize communication overhead during distributed training:
1. Same VPC Subnet: Ensures low-latency communication between training instances by keeping the network traffic within a single subnet.
2. Same AWS Region and Availability Zone: Reduces network latency further because cross-AZ communication incurs additional latency and costs.
3. Data in the Same Region and AZ: Ensures that the training data is accessed with minimal latency, improving performance during training.
This configuration optimizes communication efficiency and minimizes overhead.



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