Amazon MLA-C01 practice materials Questions Answers

Exam Code:
MLA-C01
Exam Name:
AWS Certified Machine Learning Engineer – Associate Exam
Last Update:
Jun 01,2026

85 Questions Answers Verified by Experts!


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Page: 1 / 17
Total Questions: 85

Questions & Answers PDF

• Question 1

  

  A company is planning to use Amazon Redshift ML in its primary AWS account. The source data is in an Amazon S3 bucket in a secondary account.An ML engineer needs to set up an ML pipeline in the primary account to access the S3 bucket in the secondary account. The solution must not require public IPv4 addresses.Which solution will meet these requirements?

  • Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create a VPC peering connection between the accounts. Update the VPC route tables to remove the route to 0.0.0.0/0.
  • Provision a Redshift cluster and Amazon SageMaker Studio in a VPC with no public access enabled in the primary account. Create an AWS Direct Connect connection and a transit gateway. Associate the VPCs from both accounts with the transit gateway. Update the VPC route tables to remove the route to 0.0.0.0/0.
  • Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account. Create an AWS Site-to-Site VPN connection with two encrypted IPsec tunnels between the accounts. Set up interface VPC endpoints for Amazon S3.
  • Provision a Redshift cluster and Amazon SageMaker Studio in a VPC in the primary account. Create an S3 gateway endpoint. Update the S3 bucket policy to allow IAM principals from the primary account. Set up interface VPC endpoints for SageMaker and Amazon Redshift.

  Reveal Answer Answer: D Next Question

• Question 2

  

  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?

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

  Reveal Answer Answer: B Next Question

• Question 3

  

  A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.Which solution will meet these requirements?

  • Increase the temperature parameter and the top_k parameter.
  • Increase the temperature parameter. Decrease the top_k parameter.
  • Decrease the temperature parameter. Increase the top_k parameter.
  • Decrease the temperature parameter and the top_k parameter.

  Reveal Answer Answer: D Next Question

• Question 4

  

  Case studyAn 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?

  • LightGBM
  • Linear learner
  • К-means clustering
  • Neural Topic Model (NTM)

  Reveal Answer Answer: B Next Question

• Question 5

  

  A company wants to predict the success of advertising campaigns by considering the color scheme of each advertisement. An ML engineer is preparing data for a neural network model. The dataset includes color information as categorical data.Which technique for feature engineering should the ML engineer use for the model?

  • Apply label encoding to the color categories. Automatically assign each color a unique integer.
  • Implement padding to ensure that all color feature vectors have the same length.
  • Perform dimensionality reduction on the color categories.
  • One-hot encode the color categories to transform the color scheme feature into a binary matrix.

  Reveal Answer Answer: D Next Question

• Question 6

  

  A company that has hundreds of data scientists is using Amazon SageMaker to create ML models. The models are in model groups in the SageMaker Model Registry.The data scientists are grouped into three categories: computer vision, natural language processing (NLP), and speech recognition. An ML engineer needs to implement a solution to organize theexisting models into these groups to improve model discoverability at scale. The solution must not affect the integrity of the model artifacts and their existing groupings.Which solution will meet these requirements?

  • Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.
  • Create a model group for each category. Move the existing models into these category model groups.
  • Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.
  • Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.

  Reveal Answer Answer: A Next Question

• Question 7

  

  A company wants to develop an ML model by using tabular data from its customers. The datacontains meaningful ordered features with sensitive information that should not be discarded. An ML engineer must ensure that the sensitive data is masked before another team starts to build the model.Which solution will meet these requirements?

  • Use Amazon Made to categorize the sensitive data.
  • Prepare the data by using AWS Glue DataBrew.
  • Run an AWS Batch job to change the sensitive data to random values.
  • Run an Amazon EMR job to change the sensitive data to random values.

  Reveal Answer Answer: B Next Question

• Question 8

  

  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?

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

  Reveal Answer Answer: D Next Question

• Question 9

  

  An ML engineer needs to use an ML model to predict the price of apartments in a specific location. Which metric should the ML engineer use to evaluate the model's performance?

  • Accuracy
  • Area Under the ROC Curve (AUC)
  • F1 score
  • Mean absolute error (MAE)

  Reveal Answer Answer: D Next Question

• Question 10

  

  An ML engineer needs to use AWS CloudFormation to create an ML model that an Amazon SageMaker endpoint will host.Which resource should the ML engineer declare in the CloudFormation template to meet this requirement?

  • AWS::SageMaker::Model
  • AWS::SageMaker::Endpoint
  • AWS::SageMaker::NotebookInstance
  • AWS::SageMaker::Pipeline

  Reveal Answer Answer: A Next Question

Page: 1 / 17
Total Questions: 85

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