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

Page 8 of 22
PDF with 81 Questions

A company has a large collection of chat recordings from customer interactions after a product release. An ML engineer needs to create an ML model to analyze the chat data. The ML engineer needs to determine the success of the product by reviewing customer sentiments about the product.
Which action should the ML engineer take to complete the evaluation in the LEAST amount of time?

  1. Use Amazon Rekognition to analyze sentiments of the chat conversations.
  2. Train a Naive Bayes classifier to analyze sentiments of the chat conversations.
  3. Use Amazon Comprehend to analyze sentiments of the chat conversations.
  4. Use random forests to classify sentiments of the chat conversations.

Answer(s): C



A company has a conversational AI assistant that sends requests through Amazon Bedrock to an Anthropic Claude large language model (LLM). Users report that when they ask similar questions multiple times, they sometimes receive different answers. An ML engineer needs to improve the responses to be more consistent and less random.
Which solution will meet these requirements?

  1. Increase the temperature parameter and the top_k parameter.
  2. Increase the temperature parameter. Decrease the top_k parameter.
  3. Decrease the temperature parameter. Increase the top_k parameter.
  4. Decrease the temperature parameter and the top_k parameter.

Answer(s): D



A company is using ML to predict the presence of a specific weed in a farmer's field. The company is using the Amazon SageMaker linear learner built-in algorithm with a value of multiclass_dassifier for the predictorjype hyperparameter.
What should the company do to MINIMIZE false positives?

  1. Set the value of the weight decay hyperparameter to zero.
  2. Increase the number of training epochs.
  3. Increase the value of the target_precision hyperparameter.
  4. Change the value of the predictorjype hyperparameter to regressor.

Answer(s): C



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?

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

Answer(s): A






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