Download AWS Certified Machine Learning Engineer - Associate.MLA-C01.ExamTopics.2026-04-08.194q.tqb

Vendor: Amazon
Exam Code: MLA-C01
Exam Name: AWS Certified Machine Learning Engineer - Associate
Date: Apr 08, 2026
File Size: 4 MB
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Demo Questions

Question 1
An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day.
The ML engineer needs a scalable solution that minimizes costs when the model is not in use. The solution also must maintain the model's capacity to respond to requests during times of peak usage.
Which solution will meet these requirements?
  1. Create AWS Lambda functions that have fixed concurrency to host the model. Configure the Lambda functions to automatically scale based on the number of requests to the model.
  2. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Set a static number of tasks to handle requests during times of peak usage.
  3. Deploy the model to an Amazon SageMaker endpoint. Deploy multiple copies of the model to the endpoint. Create an Application Load Balancer to route traffic between the different copies of the model at the endpoint.
  4. Deploy the model to an Amazon SageMaker endpoint. Create SageMaker endpoint auto scaling policies that are based on Amazon CloudWatch metrics to adjust the number of instances dynamically.
Correct answer: D
Question 2
A company has deployed an ML model that detects fraudulent credit card transactions in real time in a banking application. The model uses Amazon SageMaker Asynchronous Inference. Consumers are reporting delays in receiving the inference results.
An ML engineer needs to implement a solution to improve the inference performance. The solution also must provide a notification when a deviation in model quality occurs.
Which solution will meet these requirements?
  1. Use SageMaker real-time inference for inference. Use SageMaker Model Monitor for notifications about model quality.
  2. Use SageMaker batch transform for inference. Use SageMaker Model Monitor for notifications about model quality.
  3. Use SageMaker Serverless Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.
  4. Keep using SageMaker Asynchronous Inference for inference. Use SageMaker Inference Recommender for notifications about model quality.
Correct answer: A
Question 3
A company stores time-series data about user clicks in an Amazon S3 bucket. The raw data consists of millions of rows of user activity every day. ML engineers access the data to develop their ML models.
The ML engineers need to generate daily reports and analyze click trends over the past 3 days by using Amazon Athena. The company must retain the data for 30 days before archiving the data.
Which solution will provide the HIGHEST performance for data retrieval?
  1. Keep all the time-series data without partitioning in the S3 bucket. Manually move data that is older than 30 days to separate S3 buckets.
  2. Create AWS Lambda functions to copy the time-series data into separate S3 buckets. Apply S3 Lifecycle policies to archive data that is older than 30 days to S3 Glacier Flexible Retrieval.
  3. Organize the time-series data into partitions by date prefix in the S3 bucket. Apply S3 Lifecycle policies to archive partitions that are older than 30 days to S3 Glacier Flexible Retrieval.
  4. Put each day's time-series data into its own S3 bucket. Use S3 Lifecycle policies to archive S3 buckets that hold data that is older than 30 days to S3 Glacier Flexible Retrieval.
Correct answer: C
Question 4
A credit card company has a fraud detection model in production on an Amazon SageMaker endpoint. The company develops a new version of the model. The company needs to assess the new model's performance by using live data and without affecting production end users.
Which solution will meet these requirements?
  1. Set up SageMaker Debugger and create a custom rule.
  2. Set up blue/green deployments with all-at-once traffic shifting.
  3. Set up blue/green deployments with canary traffic shifting.
  4. Set up shadow testing with a shadow variant of the new model.
Correct answer: D
Question 5
An ML engineer is using a training job to fine-tune a deep learning model in Amazon SageMaker Studio. The ML engineer previously used the same pre-trained model with a similar dataset. The ML engineer expects vanishing gradient, underutilized GPU, and overfitting problems.
The ML engineer needs to implement a solution to detect these issues and to react in predefined ways when the issues occur. The solution also must provide comprehensive real-time metrics during the training.
Which solution will meet these requirements with the LEAST operational overhead?
  1. Use TensorBoard to monitor the training job. Publish the findings to an Amazon Simple Notification Service (Amazon SNS) topic. Create an AWS Lambda function to consume the findings and to initiate the predefined actions.
  2. Use Amazon CloudWatch default metrics to gain insights about the training job. Use the metrics to invoke an AWS Lambda function to initiate the predefined actions.
  3. Expand the metrics in Amazon CloudWatch to include the gradients in each training step. Use the metrics to invoke an AWS Lambda function to initiate the predefined actions.
  4. Use SageMaker Debugger built-in rules to monitor the training job. Configure the rules to initiate the predefined actions.
Correct answer: D
Question 6
A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive.
A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database.
Which solution will meet these requirements with the LEAST implementation effort?
  1. Configure dynamic data masking policies to control how sensitive data is shared with the data scientist at query time.
  2. Create a materialized view with masking logic on top of the database. Grant the necessary read permissions to the data scientist.
  3. Unload the Amazon Redshift data to Amazon S3. Use Amazon Athena to create schema-on-read with masking logic. Share the view with the data scientist.
  4. Unload the Amazon Redshift data to Amazon S3. Create an AWS Glue job to anonymize the data. Share the dataset with the data scientist.
Correct answer: A
Question 7
An ML engineer is evaluating several ML models and must choose one model to use in production. The cost of false negative predictions by the models is much higher than the cost of false positive predictions.
Which metric finding should the ML engineer prioritize the MOST when choosing the model?
  1. Low precision
  2. High precision
  3. Low recall
  4. High recall
Correct answer: D
Question 8
An ML engineer needs to use data with Amazon SageMaker Canvas to train an ML model. The data is stored in Amazon S3 and is complex in structure. The ML engineer must use a file format that minimizes processing time for the data.
Which file format will meet these requirements?
  1. CSV files compressed with Snappy
  2. JSON objects in JSONL format
  3. JSON files compressed with gzip
  4. Apache Parquet files
Correct answer: D
Question 9
An advertising company uses AWS Lake Formation to manage a data lake. The data lake contains structured data and unstructured data. The company's ML engineers are assigned to specific advertisement campaigns.
The ML engineers must interact with the data through Amazon Athena and by browsing the data directly in an Amazon S3 bucket. The ML engineers must have access to only the resources that are specific to their assigned advertisement campaigns.
Which solution will meet these requirements in the MOST operationally efficient way?
  1. Configure IAM policies on an AWS Glue Data Catalog to restrict access to Athena based on the ML engineers' campaigns.
  2. Store users and campaign information in an Amazon DynamoDB table. Configure DynamoDB Streams to invoke an AWS Lambda function to update S3 bucket policies.
  3. Use Lake Formation to authorize AWS Glue to access the S3 bucket. Configure Lake Formation tags to map ML engineers to their campaigns.
  4. Configure S3 bucket policies to restrict access to the S3 bucket based on the ML engineers' campaigns.
Correct answer: C
Question 10
A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 ТВ in size and consists of CSV, JSON, Apache Parquet, and simple text files.
The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.
Which solution will meet these requirements?
  1. Process data at each step by using Amazon SageMaker Data Wrangler. Automate the process by using Data Wrangler jobs.
  2. Use Amazon SageMaker notebooks for each data processing step. Automate the process by using Amazon EventBridge.
  3. Process data at each step by using AWS Lambda functions. Automate the process by using AWS Step Functions and Amazon EventBridge.
  4. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge.
Correct answer: D
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