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[General] MLA-C01 Valid Test Notes - MLA-C01 Valid Test Duration

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【General】 MLA-C01 Valid Test Notes - MLA-C01 Valid Test Duration

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No doubt the AWS Certified Machine Learning Engineer - Associate (MLA-C01) certification is one of the most challenging certification exams in the market. This AWS Certified Machine Learning Engineer - Associate (MLA-C01) certification exam gives always a tough time to AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam candidates. The PremiumVCEDump understands this hurdle and offers recommended and real Amazon MLA-C01 Exam Practice questions in three different formats. These formats hold high demand in the market and offer a great solution for quick and complete AWS Certified Machine Learning Engineer - Associate (MLA-C01) exam preparation.
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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q142-Q147):NEW QUESTION # 142
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?
  • A. Use Amazon Bedrock Provisioned Throughput to process queries more efficiently.
  • B. Use auto-summarization on the retrieved articles by using Amazon SageMaker JumpStart.
  • C. Use a reranker model before passing the articles to the foundation model (FM).
  • D. Use Amazon Athena to pre-filter the articles based on metadata before retrieval.
Answer: C
Explanation:
In a Retrieval Augmented Generation (RAG) architecture, retrieval quality directly impacts response accuracy. AWS documentation for Bedrock and OpenSearch highlights the use of reranker models to improve relevance after initial vector search retrieval.
Vector search retrieves documents based on embedding similarity, but the top results are not always the most contextually relevant. A reranker model evaluates the retrieved documents against the user query and reorders them based on semantic relevance before sending them to the foundation model.
Option A improves readability but does not improve retrieval relevance. Option C filters data before retrieval, which can reduce recall. Option D improves performance, not relevance.
AWS explicitly recommends reranking as a best practice for improving answer quality in RAG systems.
Therefore, Option B is the correct solution.

NEW QUESTION # 143
A data scientist uses logistic regression to build a fraud detection model. While the model accuracy is 99%, 90% of the fraud cases are not detected by the model.
What action will definitively help the model detect more than 10% of fraud cases?
  • A. Using undersampling to balance the dataset
  • B. Decreasing the class probability threshold
  • C. Using oversampling to balance the dataset
  • D. Using regularization to reduce overfitting
Answer: B
Explanation:
Decreasing the class probability threshold makes the model more sensitive and, therefore, marks more cases as the positive class, which is fraud in this case. This will increase the likelihood of fraud detection. However, it comes at the price of lowering precision.

NEW QUESTION # 144
An ML engineer has an Amazon Comprehend custom model in Account A in the us-east-1 Region. The ML engineer needs to copy the model to Account # in the same Region.
Which solution will meet this requirement with the LEAST development effort?
  • A. Create an AWS Site-to-Site VPN connection between Account A and Account # to transfer the model.
  • B. Create a resource-based IAM policy. Use the Amazon Comprehend ImportModel API operation to copy the model to Account B.
  • C. Use AWS DataSync to replicate the model from Account A to Account B.
  • D. Use Amazon S3 to make a copy of the model. Transfer the copy to Account B.
Answer: B
Explanation:
Amazon Comprehend provides the ImportModel API operation, which allows you to copy a custom model between AWS accounts. By creating a resource-based IAM policy on the model in Account A, you can grant Account B the necessary permissions to access and import the model. This approach requires minimal development effort and is the AWS-recommended method for sharing custom models across accounts.

NEW QUESTION # 145
A company uses an Amazon EMR cluster to run a data ingestion process for an ML model. An ML engineer notices that the processing time is increasing.
Which solution will reduce the processing time MOST cost-effectively?
  • A. Use On-Demand Instances to increase the number of core nodes.
  • B. Use Spot Instances to increase the number of task nodes.
  • C. Use Spot Instances to increase the number of core nodes.
  • D. Use Spot Instances to increase the number of primary nodes.
Answer: B
Explanation:
Amazon EMR clusters consist of primary, core, and task nodes, each with a distinct role. The primary node manages the cluster, core nodes store data and run tasks, and task nodes only run tasks without storing data.
AWS documentation recommends using task nodes for scaling compute capacity when workloads are compute-intensive, such as data ingestion and transformation pipelines.
To reduce processing time cost-effectively, AWS strongly advises using Spot Instances for task nodes. Spot Instances provide the same compute capacity as On-Demand Instances but at a significantly reduced cost, often up to 90% lower. Because task nodes do not store HDFS data, they can be safely interrupted without risking data loss.
Increasing the number of primary nodes is not supported by EMR and would not improve performance.
Increasing core nodes affects both storage and compute and is more expensive, especially when using On- Demand Instances. Option D is therefore the least cost-effective.
AWS EMR best practices explicitly state that scaling out with Spot task nodes is the preferred way to improve performance for transient, parallel workloads such as ETL, ingestion, and feature preparation.
Therefore, Option C is the most cost-effective and AWS-recommended solution.

NEW QUESTION # 146
Hotspot Question
An ML engineer is building a generative AI application on Amazon Bedrock by using large language models (LLMs).
Select the correct generative AI term from the following list for each description. Each term should be selected one time or not at all. (Select three.)
- Embedding
- Retrieval Augmented Generation (RAG)
- Temperature
- Token

Answer:
Explanation:


NEW QUESTION # 147
......
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