Cisco
CompTIA
ISC
EC-Council
EXIN
Fortinet
ITIL
Juniper
Microsoft
VMware
Avaya
SAP
Google
NVIDIA
Salesforce
Amazon
Palo Alto Networks
ISC2
Splunk
ServiceNow
All Vendors
  2. Home
  3. Exams
  4. Amazon
  5. AWS Certified Machine Learning Engineer - Associate MLA-C01

Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 Exam
AWS Certified Machine Learning Engineer - Associate MLA-C01 (Page 8 )

Updated On: 9-Feb-2026
Page 8 of 47
PDF with 230 Questions

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.

Answer(s): B

Explanation:

Amazon Transcribe is well-suited for converting audio data into text, including Spanish.
Amazon Translate can efficiently translate Spanish text into English if needed.

Amazon Bedrock, with the Jurassic model, is designed for tasks like text summarization and can handle large language models (LLMs) seamlessly. This combination provides a low-code, managed solution to process audio, video, and text data with minimal time and effort.



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.

Answer(s): A

Explanation:

This solution is the most efficient and involves the least operational overhead:
Amazon Kinesis data streams efficiently handle real-time ingestion of high-volume streaming data.

Amazon Managed Service for Apache Flink provides a fully managed environment for stream processing with built-in support for RANDOM_CUT_FOREST, an algorithm designed for anomaly detection in real-time streaming data.
This approach eliminates the need for deploying and managing additional infrastructure like SageMaker endpoints, Lambda functions, or external tools, making it the most scalable and operationally simple solution.



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

Explanation:

Amazon Comprehend is a fully managed natural language processing (NLP) service that includes a built-in sentiment analysis feature. It can quickly and efficiently analyze text data to determine whether the sentiment is positive, negative, neutral, or mixed. Using Amazon Comprehend requires minimal setup and provides accurate results without the need to train and deploy custom models, making it the fastest and most efficient solution for this task.



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

Explanation:

The temperature parameter controls the randomness in the model's responses. Lowering the temperature makes the model produce more deterministic and consistent answers.
The top_k parameter limits the number of tokens considered for generating the next word. Reducing top_k further constrains the model's options, ensuring more predictable responses.
By decreasing both parameters, the responses become more focused and consistent, reducing variability in similar queries.



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_classifier for the predictor_type 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 predictor_type hyperparameter to regressor.

Answer(s): C

Explanation:

The target_precision hyperparameter in the Amazon SageMaker linear learner controls the trade-off between precision and recall for the model. Increasing the target_precision prioritizes minimizing false positives by making the model more cautious in its predictions. This approach is effective for use cases where false positives have higher consequences than false negatives.






Post your Comments and Discuss Amazon AWS Certified Machine Learning Engineer - Associate MLA-C01 exam prep with other Community members:

Join the AWS Certified Machine Learning Engineer - Associate MLA-C01 Discussion