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Free AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam Braindumps
AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions 57–64 (page: 9)

Updated On: 15-Apr-2026
Page 9 of 33
PDF with 270 Questions

A company has an ML model that needs to run one time each night to predict stock values. The model input is 3 MB of data that is collected during the current day. The model produces the predictions for the next day. The prediction process takes less than 1 minute to finish running.

How should the company deploy the model on Amazon SageMaker to meet these requirements?

  1. Use a multi-model serverless endpoint. Enable caching.
  2. Use an asynchronous inference endpoint. Set the InitialInstanceCount parameter to 0.
  3. Use a real-time endpoint. Configure an auto scaling policy to scale the model to 0 when the model is not in use.
  4. Use a serverless inference endpoint. Set the MaxConcurrency parameter to 1.

Answer(s): D

Explanation:

A serverless inference endpoint in Amazon SageMaker is ideal for use cases where the model is invoked infrequently, such as running one time each night. It eliminates the cost of idle resources when the model is not in use. Setting the MaxConcurrency parameter to 1 ensures cost-efficiency while supporting the required single nightly invocation. This solution minimizes costs and matches the requirement to process a small amount of data quickly.



An ML engineer trained an ML model on Amazon SageMaker AI to detect automobile accidents from closed- circuit TV footage. The ML engineer used SageMaker Data Wrangler to create a training dataset of images of accidents and non-accidents.

The model performed well during training and validation. However, the model is underperforming in production because of variations in the quality of the images from various cameras.

Which solution will improve the model's accuracy in the LEAST amount of time?

  1. Collect more images from all the cameras. Use Data Wrangler to prepare a new training dataset.
  2. Recreate the training dataset by using the Data Wrangler corrupt image transform. Specify the impulse noise option.
  3. Recreate the training dataset by using the Data Wrangler enhance image contrast transform. Specify the Gamma contrast option.
  4. Recreate the training dataset by using the Data Wrangler resize image transform. Crop all images to the same size.

Answer(s): B

Explanation:

The model is underperforming in production due to variations in image quality from different cameras. Using the corrupt image transform with the impulse noise option in SageMaker Data Wrangler simulates real- world noise and variations in the training dataset. This approach helps the model become more robust to inconsistencies in image quality, improving its accuracy in production without the need to collect and process new data, thereby saving time.



A company has an application that uses different APIs to generate embeddings for input text. The company needs to implement a solution to automatically rotate the API tokens every 3 months.

Which solution will meet this requirement?

  1. Store the tokens in AWS Secrets Manager. Create an AWS Lambda function to perform the rotation.
  2. Store the tokens in AWS Systems Manager Parameter Store. Create an AWS Lambda function to perform the rotation.
  3. Store the tokens in AWS Key Management Service (AWS KMS). Use an AWS managed key to perform the rotation.
  4. Store the tokens in AWS Key Management Service (AWS KMS). Use an AWS owned key to perform the rotation.

Answer(s): A

Explanation:

AWS Secrets Manager is designed for securely storing, managing, and automatically rotating secrets, including API tokens. By configuring a Lambda function for custom rotation logic, the solution can automatically rotate the API tokens every 3 months as required. Secrets Manager simplifies secret management and integrates seamlessly with other AWS services, making it the ideal choice for this use case.



An ML engineer receives datasets that contain missing values, duplicates, and extreme outliers. The ML engineer must consolidate these datasets into a single data frame and must prepare the data for ML.

Which solution will meet these requirements?

  1. Use Amazon SageMaker Data Wrangler to import the datasets and to consolidate them into a single data frame. Use the cleansing and enrichment functionalities to prepare the data.
  2. Use Amazon SageMaker Ground Truth to import the datasets and to consolidate them into a single data frame. Use the human-in-the-loop capability to prepare the data.
  3. Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon Q Developer to generate code snippets that will prepare the data.
  4. Manually import and merge the datasets. Consolidate the datasets into a single data frame. Use Amazon SageMaker data labeling to prepare the data.

Answer(s): A

Explanation:

Amazon SageMaker Data Wrangler provides a comprehensive solution for importing, consolidating, and preparing datasets for ML. It offers tools to handle missing values, duplicates, and outliers through its built-in cleansing and enrichment functionalities, allowing the ML engineer to efficiently prepare the data in a single environment with minimal manual effort.



A company has historical data that shows whether customers needed long-term support from company staff. The company needs to develop an ML model to predict whether new customers will require long-term support.

Which modeling approach should the company use to meet this requirement?

  1. Anomaly detection
  2. Linear regression
  3. Logistic regression
  4. Semantic segmentation

Answer(s): C

Explanation:

Logistic regression is a suitable modeling approach for this requirement because it is designed for binary classification problems, such as predicting whether a customer will require long-term support ("yes" or "no").
It calculates the probability of a particular class and is widely used for tasks like this where the outcome is categorical.



An ML engineer has developed a binary classification model outside of Amazon SageMaker AI. The ML engineer needs to make the model accessible to a SageMaker Canvas user for additional tuning.

The model artifacts are stored in an Amazon S3 bucket. The ML engineer and the Canvas user are part of the same SageMaker AI domain.

Which combination of requirements must be met so that the ML engineer can share the model with the Canvas user? (Choose two.)

  1. The ML engineer and the Canvas user must be in separate SageMaker AI domains.
  2. The Canvas user must have permissions to access the S3 bucket where the model artifacts are stored.
  3. The model must be registered in the SageMaker Model Registry.
  4. The ML engineer must host the model on AWS Marketplace.
  5. The ML engineer must deploy the model to a SageMaker AI endpoint.

Answer(s): B,C

Explanation:

The SageMaker Canvas user needs permissions to access the Amazon S3 bucket where the model artifacts are stored to retrieve the model for use in Canvas.
Registering the model in the SageMaker Model Registry allows the model to be tracked and managed within the SageMaker ecosystem. This makes it accessible for tuning and deployment through SageMaker Canvas.
This combination ensures proper access control and integration within SageMaker, enabling the Canvas user to work with the model.



A company is building a deep learning model on Amazon SageMaker AI. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset.

Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?

  1. Hyperband
  2. Grid search
  3. Bayesian optimization
  4. Random search

Answer(s): A

Explanation:

Hyperband is a hyperparameter tuning strategy designed to minimize computation time by adaptively allocating resources to promising configurations and terminating underperforming ones early. It efficiently balances exploration and exploitation, making it ideal for large datasets and deep learning models where training can be computationally expensive.



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?

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

Answer(s): D

Explanation:

S3 Gateway Endpoint: Allows private access to S3 from within a VPC without requiring a public IPv4 address, ensuring that data transfer between the primary and secondary accounts is secure and private.
Bucket Policy Update: The S3 bucket policy in the secondary account must explicitly allow access from the primary account's IAM principals to provide the necessary permissions.
Interface VPC Endpoints: Required for private communication between the VPC and Amazon SageMaker and Amazon Redshift services, ensuring the solution operates without public internet access.
This configuration meets the requirement to avoid public IPv4 addresses and allows secure and private communication between the accounts.



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