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Amazon Web Services Updated MLA-C01 Exam Questions and Answers by baani

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Amazon Web Services MLA-C01 Exam Overview :

Exam Name: AWS Certified Machine Learning Engineer - Associate
Exam Code: MLA-C01 Dumps
Vendor: Amazon Web Services Certification: AWS Certified Associate
Questions: 241 Q&A's Shared By: baani
Question 20

An ML engineering team is spread across multiple locations. When the lead ML engineer opens an Amazon SageMaker AI notebook, the ML engineer does not see the latest merged notebook made by other team members from a Git repository.

The lead ML engineer must see the latest SageMaker AI notebook updates.

Which solution will meet this requirement?

Options:

A.

Run the !git pull origin master command.

B.

Run the !git commit command.

C.

Run the !git push origin master command.

D.

Run the !git branch command.

Discussion
Question 21

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?

Options:

A.

Enable S3 bucket versioning.

B.

Configure S3 Object Lock settings for each user.

C.

Add cross-origin resource sharing (CORS) policies to the S3 buckets.

D.

Create IAM policies. Attach the policies to IAM users or IAM roles.

Discussion
Question 22

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?

Options:

A.

Configure the competitor ' s name as a blocked phrase in Amazon Q Business.

B.

Configure an Amazon Q Business retriever to exclude the competitor’s name.

C.

Configure an Amazon Kendra retriever for Amazon Q Business to build indexes that exclude the competitor ' s name.

D.

Configure document attribute boosting in Amazon Q Business to deprioritize the competitor ' s name.

Discussion
Question 23

An ML engineer is using Amazon SageMaker AI to train an ML model. The ML engineer needs to use SageMaker AI automatic model tuning (AMT) features to tune the model hyperparameters over a large parameter space.

The model has 20 categorical hyperparameters and 7 continuous hyperparameters that can be tuned. The ML engineer needs to run the tuning job a maximum of 1,000 times. The ML engineer must ensure that each parameter trial is built based on the performance of the previous trial.

Which solution will meet these requirements?

Options:

A.

Define the search space as categorical parameters of 1,000 possible combinations. Use grid search.

B.

Define the search space as continuous parameters. Use random search. Set the maximum number of tuning jobs to 1,000.

C.

Define the search space as categorical parameters and continuous parameters. Use Bayesian optimization. Set the maximum number of training jobs to 1,000.

D.

Define the search space as categorical parameters and continuous parameters. Use grid search. Set the maximum number of tuning jobs to 1,000.

Discussion
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