100% PASS QUIZ 2025 RELIABLE MLA-C01: USEFUL AWS CERTIFIED MACHINE LEARNING ENGINEER - ASSOCIATE DUMPS

100% Pass Quiz 2025 Reliable MLA-C01: Useful AWS Certified Machine Learning Engineer - Associate Dumps

100% Pass Quiz 2025 Reliable MLA-C01: Useful AWS Certified Machine Learning Engineer - Associate Dumps

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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q68-Q73):

NEW QUESTION # 68
A company has used Amazon SageMaker to deploy a predictive ML model in production. The company is using SageMaker Model Monitor on the model. After a model update, an ML engineer notices data quality issues in the Model Monitor checks.
What should the ML engineer do to mitigate the data quality issues that Model Monitor has identified?

  • A. Include additional data in the existing training set for the model. Retrain and redeploy the model.
  • B. Create a new baseline from the latest dataset. Update Model Monitor to use the new baseline for evaluations.
  • C. Initiate a manual Model Monitor job that uses the most recent production data.
  • D. Adjust the model's parameters and hyperparameters.

Answer: B

Explanation:
When Model Monitor identifies data quality issues, it might be due to a shift in the data distribution compared to the original baseline. By creating a new baseline using the most recent production data and updating Model Monitor to evaluate against this baseline, the ML engineer ensures that the monitoring is aligned with the current data patterns. This approach mitigates false positives and reflects the updated data characteristics without immediately retraining the model.


NEW QUESTION # 69
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?

  • A. Use Amazon Rekognition to analyze sentiments of the chat conversations.
  • B. Train a Naive Bayes classifier to analyze sentiments of the chat conversations.
  • C. Use Amazon Comprehend to analyze sentiments of the chat conversations.
  • D. Use random forests to classify sentiments of the chat conversations.

Answer: 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.


NEW QUESTION # 70
An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day.
The ML engineer needs a scalable solution that minimizes costs when the model is not in use. The solution also must maintain the model's capacity to respond to requests during times of peak usage.
Which solution will meet these requirements?

  • A. Deploy the model to an Amazon SageMaker endpoint. Create SageMaker endpoint auto scaling policies that are based on Amazon CloudWatch metrics to adjust the number of instances dynamically.
  • B. Create AWS Lambda functions that have fixed concurrency to host the model. Configure the Lambda functions to automatically scale based on the number of requests to the model.
  • C. Deploy the model to an Amazon SageMaker endpoint. Deploy multiple copies of the model to the endpoint. Create an Application Load Balancer to route traffic between the different copies of the model at the endpoint.
  • D. Deploy the model on an Amazon Elastic Container Service (Amazon ECS) cluster that uses AWS Fargate. Set a static number of tasks to handle requests during times of peak usage.

Answer: A


NEW QUESTION # 71
A company is planning to create several ML prediction models. The training data is stored in Amazon S3. The entire dataset is more than 5 ## in size and consists of CSV, JSON, Apache Parquet, and simple text files.
The data must be processed in several consecutive steps. The steps include complex manipulations that can take hours to finish running. Some of the processing involves natural language processing (NLP) transformations. The entire process must be automated.
Which solution will meet these requirements?

  • A. Process data at each step by using Amazon SageMaker Data Wrangler. Automate the process by using Data Wrangler jobs.
  • B. Use Amazon SageMaker Pipelines to create a pipeline of data processing steps. Automate the pipeline by using Amazon EventBridge.
  • C. Process data at each step by using AWS Lambda functions. Automate the process by using AWS Step Functions and Amazon EventBridge.
  • D. Use Amazon SageMaker notebooks for each data processing step. Automate the process by using Amazon EventBridge.

Answer: B

Explanation:
Amazon SageMaker Pipelines is designed for creating, automating, and managing end-to-end ML workflows, including complex data preprocessing tasks. It supports handling large datasets and can integrate with custom steps, such as NLP transformations. By combining SageMaker Pipelines with Amazon EventBridge, the entire workflow can be triggered and automated efficiently, meeting the requirements for scalability, automation, and processing complexity.


NEW QUESTION # 72
A company wants to develop an ML model by using tabular data from its customers. The data contains meaningful ordered features with sensitive information that should not be discarded. An ML engineer must ensure that the sensitive data is masked before another team starts to build the model.
Which solution will meet these requirements?

  • A. Use Amazon Made to categorize the sensitive data.
  • B. Run an AWS Batch job to change the sensitive data to random values.
  • C. Prepare the data by using AWS Glue DataBrew.
  • D. Run an Amazon EMR job to change the sensitive data to random values.

Answer: C

Explanation:
AWS Glue DataBrew provides an easy-to-use interface for preparing and transforming data, including masking or obfuscating sensitive information. It offers built-in data masking features, allowing the ML engineer to handle sensitive data securely while retaining its structure and meaning. This solution is efficient and requires minimal coding, making it ideal for ensuring sensitive data is masked before model building begins.


NEW QUESTION # 73
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