START EXAM PREPARATION WITH REAL AND VALID AMAZON MLA-C01 EXAM QUESTIONS​

Start Exam Preparation with Real and Valid Amazon MLA-C01 Exam Questions​

Start Exam Preparation with Real and Valid Amazon MLA-C01 Exam Questions​

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q81-Q86):

NEW QUESTION # 81
A company is using an AWS Lambda function to monitor the metrics from an ML model. An ML engineer needs to implement a solution to send an email message when the metrics breach a threshold.
Which solution will meet this requirement?

  • A. Log the metrics from the Lambda function to Amazon CloudWatch. Configure an Amazon CloudFront rule to send the email message.
  • B. Log the metrics from the Lambda function to Amazon CloudFront. Configure an Amazon CloudWatch alarm to send the email message.
  • C. Log the metrics from the Lambda function to Amazon CloudWatch. Configure a CloudWatch alarm to send the email message.
  • D. Log the metrics from the Lambda function to AWS CloudTrail. Configure a CloudTrail trail to send the email message.

Answer: A

Explanation:
Logging the metrics to Amazon CloudWatch allows the metrics to be tracked and monitored effectively.
CloudWatch Alarms can be configured to trigger when metrics breach a predefined threshold.
The alarm can be set to notify through Amazon Simple Notification Service (SNS), which can send email messages to the configured recipients.
This is the standard and most efficient way to achieve the desired functionality.


NEW QUESTION # 82
A company has an ML model that generates text descriptions based on images that customers upload to the company's website. The images can be up to 50 MB in total size.
An ML engineer decides to store the images in an Amazon S3 bucket. The ML engineer must implement a processing solution that can scale to accommodate changes in demand.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Create an AWS Batch job that uses an Amazon Elastic Container Service (Amazon ECS) cluster.Specify a list of images to process for each AWS Batch job.
  • B. Create an Amazon SageMaker Asynchronous Inference endpoint and a scaling policy. Run a script to make an inference request for each image.
  • C. Create an Amazon SageMaker batch transform job to process all the images in the S3 bucket.
  • D. Create an Amazon Elastic Kubernetes Service (Amazon EKS) cluster that uses Karpenter for auto scaling. Host the model on the EKS cluster. Run a script to make an inference request for each image.

Answer: B

Explanation:
SageMaker Asynchronous Inference is designed for processing large payloads, such as images up to 50 MB, and can handle requests that do not require an immediate response.
It scales automatically based on the demand, minimizing operational overhead while ensuring cost-efficiency.
A script can be used to send inference requests for each image, and the results can be retrieved asynchronously. This approach is ideal for accommodating varying levels of traffic with minimal manual intervention.


NEW QUESTION # 83
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?

  • A. Anomaly detection
  • B. Logistic regression
  • C. Semantic segmentation
  • D. Linear regression

Answer: B

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.


NEW QUESTION # 84
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:

Explanation:

* Text representation of basic units of data processed by LLMs:Token
* High-dimensional vectors that contain the semantic meaning of text:Embedding
* Enrichment of information from additional data sources to improve a generated response:
Retrieval Augmented Generation (RAG)
Comprehensive Detailed Explanation
* Token:
* Description: A token represents the smallest unit of text (e.g., a word or part of a word) that an LLM processes. For example, "running" might be split into two tokens: "run" and "ing."
* Why?Tokens are the fundamental building blocks for LLM input and output processing, ensuring that the model can understand and generate text efficiently.
* Embedding:
* Description: High-dimensional vectors that encode the semantic meaning of text. These vectors are representations of words, sentences, or even paragraphs in a way that reflects their relationships and meaning.
* Why?Embeddings are essential for enabling similarity search, clustering, or any task requiring semantic understanding. They allow the model to "understand" text contextually.
* Retrieval Augmented Generation (RAG):
* Description: A technique where information is enriched or retrieved from external data sources (e.g., knowledge bases or document stores) to improve the accuracy and relevance of a model's generated responses.
* Why?RAG enhances the generative capabilities of LLMs by grounding their responses in factual and up-to-date information, reducing hallucinations in generated text.
By matching these terms to their respective descriptions, the ML engineer can effectively leverage these concepts to build robust and contextually aware generative AI applications on Amazon Bedrock.


NEW QUESTION # 85
A company has implemented a data ingestion pipeline for sales transactions from its ecommerce website. The company uses Amazon Data Firehose to ingest data into Amazon OpenSearch Service. The buffer interval of the Firehose stream is set for 60 seconds. An OpenSearch linear model generates real-time sales forecasts based on the data and presents the data in an OpenSearch dashboard.
The company needs to optimize the data ingestion pipeline to support sub-second latency for the real-time dashboard.
Which change to the architecture will meet these requirements?

  • A. Increase the buffer interval of the Firehose stream from 60 seconds to 120 seconds.
  • B. Use zero buffering in the Firehose stream. Tune the batch size that is used in the PutRecordBatch operation.
  • C. Replace the Firehose stream with an Amazon Simple Queue Service (Amazon SQS) queue.
  • D. Replace the Firehose stream with an AWS DataSync task. Configure the task with enhanced fan-out consumers.

Answer: B

Explanation:
Amazon Kinesis Data Firehose allows for near real-time data streaming. Setting thebuffering hintsto zero or a very small value minimizes the buffering delay and ensures that records are delivered to the destination (Amazon OpenSearch Service) as quickly as possible. Additionally, tuning thebatch sizein thePutRecordBatchoperation can further optimize the data ingestion for sub-second latency. This approach minimizes latency while maintaining the operational simplicity of using Firehose.


NEW QUESTION # 86
......

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