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

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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: harlie
Question 24

An ML engineer is deploying a generative AI model-based customer support agent that uses Amazon SageMaker AI for inference. The customer support agent must respond to customer questions about topics such as shipping policies, refund processes, and account management. The generative AI model generates one token at a time.

Customers report dissatisfaction with how long the customer support agent takes to generate lengthy responses to questions. The ML engineer must apply an inference optimization technique to improve the performance of the customer support agent.

Which solution will meet this requirement?

Options:

A.

Compilation

B.

Speculative decoding

C.

Quantization

D.

Fast model loading

Discussion
Question 25

An ML engineer has trained an ML model by using Amazon SageMaker AI. The ML engineer determines that the model is overfitting and that the training data contains unnecessary features. The ML engineer must reduce the overfitting and the impact of the unnecessary features.

Which solution will meet these requirements?

Options:

A.

Apply L1 regularization to the training data. Retrain the model.

B.

Use SageMaker Debugger to apply L1 regularization to the running model.

C.

Increase the number of training iterations. Retrain the model.

D.

Decrease the number of training iterations. Retrain the model.

Discussion
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Question 26

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 the existing 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?

Options:

A.

Create a custom tag for each of the three categories. Add the tags to the model packages in the SageMaker Model Registry.

B.

Create a model group for each category. Move the existing models into these category model groups.

C.

Use SageMaker ML Lineage Tracking to automatically identify and tag which model groups should contain the models.

D.

Create a Model Registry collection for each of the three categories. Move the existing model groups into the collections.

Discussion
Question 27

A company is developing a customer support AI assistant by using an Amazon Bedrock Retrieval Augmented Generation (RAG) pipeline. The AI assistant retrieves articles from a knowledge base stored in Amazon S3. The company uses Amazon OpenSearch Service to index the knowledge base. The AI assistant uses an Amazon Bedrock Titan Embeddings model for vector search.

The company wants to improve the relevance of the retrieved articles to improve the quality of the AI assistant ' s answers.

Which solution will meet these requirements?

Options:

A.

Use auto-summarization on the retrieved articles by using Amazon SageMaker JumpStart.

B.

Use a reranker model before passing the articles to the foundation model (FM).

C.

Use Amazon Athena to pre-filter the articles based on metadata before retrieval.

D.

Use Amazon Bedrock Provisioned Throughput to process queries more efficiently.

Discussion
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