AWS Certified Machine Learning Engineer - Associate MLA-C01 Practice Test

AWS Certified Machine Learning Engineer - Associate MLA-C01 Practice Test practice questions, page 7 of 11, with answer review and explanations.

Question 31

A company is gathering audio, video, and text data in various languages. The company needs to use a large language model (LLM) to summarize the gathered data that is in Spanish. Which solution will meet these requirements in the LEAST amount of time?

  1. Train and deploy a model in Amazon SageMaker to convert the data into English text. Train and deploy an LLM in SageMaker to summarize the text.
  2. Use Amazon Transcribe and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Jurassic model to summarize the text.
  3. Use Amazon Rekognition and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Anthropic Claude model to summarize the text.
  4. Use Amazon Comprehend and Amazon Translate to convert the data into English text. Use Amazon Bedrock with the Stable Diffusion model to summarize the text.

Question 32

A financial company receives a high volume of real-time market data streams from an external provider. The streams consist of thousands of JSON records every second. The company needs to implement a scalable solution on AWS to identify anomalous data points. Which solution will meet these requirements with the LEAST operational overhead?

  1. Ingest real-time data into Amazon Kinesis data streams. Use the built-in RANDOM_CUT_FOREST function in Amazon Managed Service for Apache Flink to process the data streams and to detect data anomalies.
  2. Ingest real-time data into Amazon Kinesis data streams. Deploy an Amazon SageMaker endpoint for real-time outlier detection. Create an AWS Lambda function to detect anomalies. Use the data streams to invoke the Lambda function.
  3. Ingest real-time data into Apache Kafka on Amazon EC2 instances. Deploy an Amazon SageMaker endpoint for real-time outlier detection. Create an AWS Lambda function to detect anomalies. Use the data streams to invoke the Lambda function.
  4. Send real-time data to an Amazon Simple Queue Service (Amazon SQS) FIFO queue. Create an AWS Lambda function to consume the queue messages. Program the Lambda function to start an AWS Glue extract, transform, and load (ETL) job for batch processing and anomaly detection.

Question 33

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.

Question 34

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.

Question 35

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.