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Free AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam Braindumps
AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions 49–56 (page: 8)

Updated On: 15-Apr-2026
Page 8 of 33
PDF with 270 Questions

A credit card company has a fraud detection model in production on an Amazon SageMaker AI 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.

Answer(s): D

Explanation:

Shadow testing allows you to send a copy of live production traffic to a shadow variant of the new model while keeping the existing production model unaffected. This enables you to evaluate the performance of the new model in real-time with live data without impacting end users. SageMaker endpoints support this setup by allowing traffic mirroring to the shadow variant, making it an ideal solution for assessing the new model's performance.



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.

Answer(s): C

Explanation:

Partitioning the time-series data by date prefix in the S3 bucket significantly improves query performance in Amazon Athena by reducing the amount of data that needs to be scanned during queries. This allows the ML engineers to efficiently analyze trends over specific time periods, such as the past 3 days. Applying S3 Lifecycle policies to archive partitions older than 30 days to S3 Glacier Flexible Retrieval ensures cost- effective data retention and storage management while maintaining high performance for recent data retrieval.



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.

Answer(s): A

Explanation:

SageMaker real-time inference is designed for low-latency, real-time use cases, such as detecting fraudulent transactions in banking applications. It eliminates the delays associated with SageMaker Asynchronous Inference, improving inference performance.
SageMaker Model Monitor provides tools to monitor deployed models for deviations in data quality, model performance, and other metrics. It can be configured to send notifications when a deviation in model quality is detected, ensuring the system remains reliable.



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.

Answer(s): D

Explanation:

Amazon SageMaker endpoints support auto scaling, which dynamically adjusts the number of instances based on metrics such as incoming request traffic. This ensures cost efficiency when the model is not in use while maintaining the ability to handle peak traffic during high-demand periods. By leveraging CloudWatch metrics to trigger scaling, the solution minimizes costs and meets the requirement for scalability and responsiveness.



A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker AI compute costs reach a specific threshold.

Which solution will meet these requirements?

  1. Add resource tagging by editing the SageMaker AI user profile in the SageMaker AI domain. Configure AWS Cost Explorer to send an alert when the threshold is reached.
  2. Add resource tagging by editing the SageMaker AI user profile in the SageMaker AI domain. Configure AWS Budgets to send an alert when the threshold is reached.
  3. Add resource tagging by editing each user's IAM profile. Configure AWS Cost Explorer to send an alert when the threshold is reached.
  4. Add resource tagging by editing each user's IAM profile. Configure AWS Budgets to send an alert when the threshold is reached.

Answer(s): B

Explanation:

Adding resource tagging to the SageMaker user profile enables tracking and monitoring of costs associated with specific SageMaker resources.
AWS Budgets allows setting thresholds and automated alerts for costs and usage, making it the ideal service to notify the ML engineer when compute costs reach a specified limit.
This solution is efficient and integrates seamlessly with SageMaker and AWS cost management tools.



A company uses Amazon SageMaker for its ML workloads. The company's ML engineer receives a 50 MB Apache Parquet data file to build a fraud detection model. The file includes several correlated columns that are not required.

What should the ML engineer do to drop the unnecessary columns in the file with the LEAST effort?

  1. Download the file to a local workstation. Perform one-hot encoding by using a custom Python script.
  2. Create an Apache Spark job that uses a custom processing script on Amazon EMR.
  3. Create a SageMaker processing job by calling the SageMaker Python SDK.
  4. Create a data flow in SageMaker Data Wrangler. Configure a transform step.

Answer(s): D

Explanation:

SageMaker Data Wrangler provides a no-code/low-code interface for preparing and transforming data, including dropping unnecessary columns. By creating a data flow and configuring a transform step, the ML engineer can easily remove correlated or unneeded columns from the Parquet file with minimal effort. This approach avoids the need for custom coding or managing additional infrastructure.



A company is creating an application that will recommend products for customers to purchase. The application will make API calls to Amazon Q Business. The company must ensure that responses from Amazon Q Business do not include the name of the company's main competitor.

Which solution will meet this requirement?

  1. Configure the competitor's name as a blocked phrase in Amazon Q Business.
  2. Configure an Amazon Q Business retriever to exclude the competitor's name.
  3. Configure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude the competitor's name.
  4. Configure document attribute boosting in Amazon Q Business to deprioritize the competitor's name.

Answer(s): A

Explanation:

Amazon Q Business allows configuring blocked phrases to exclude specific terms or phrases from the responses. By adding the competitor's name as a blocked phrase, the company can ensure that it will not appear in the API responses, meeting the requirement efficiently with minimal configuration.



An ML engineer needs to use Amazon SageMaker AI to fine-tune a large language model (LLM) for text summarization. The ML engineer must follow a low-code no-code (LCNC) approach.

Which solution will meet these requirements?

  1. Use SageMaker Studio to fine-tune an LLM that is deployed on Amazon EC2 instances.
  2. Use SageMaker Autopilot to fine-tune an LLM that is deployed by a custom API endpoint.
  3. Use SageMaker Autopilot to fine-tune an LLM that is deployed on Amazon EC2 instances.
  4. Use SageMaker Autopilot to fine-tune an LLM that is deployed by SageMaker JumpStart.

Answer(s): D

Explanation:

SageMaker JumpStart provides access to pre-trained models, including large language models (LLMs), which can be easily deployed and fine-tuned with a low-code/no-code (LCNC) approach. Using SageMaker Autopilot with JumpStart simplifies the fine-tuning process by automating model optimization and reducing the need for extensive coding, making it the ideal solution for this requirement.



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