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[General] New MLA-C01 Test Notes, MLA-C01 Practice Engine

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【General】 New MLA-C01 Test Notes, MLA-C01 Practice Engine

Posted at yesterday 00:29      View：25 | Replies：1        Print      Only Author   [Copy Link] 1#
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Amazon AWS Certified Machine Learning Engineer - Associate Sample Questions (Q34-Q39):NEW QUESTION # 34
A company is using an Amazon Redshift database as its single data source. Some of the data is sensitive.
A data scientist needs to use some of the sensitive data from the database. An ML engineer must give the data scientist access to the data without transforming the source data and without storing anonymized data in the database.
Which solution will meet these requirements with the LEAST implementation effort?
  • A. Configure dynamic data masking policies to control how sensitive data is shared with the data scientist at query time.
  • B. Create a materialized view with masking logic on top of the database. Grant the necessary read permissions to the data scientist.
  • C. Unload the Amazon Redshift data to Amazon S3. Create an AWS Glue job to anonymize the data.Share the dataset with the data scientist.
  • D. Unload the Amazon Redshift data to Amazon S3. Use Amazon Athena to create schema-on-read with masking logic. Share the view with the data scientist.
Answer: A
Explanation:
Dynamic data maskingallows you to control how sensitive data is presented to users at query time, without modifying or storing transformed versions of the source data. Amazon Redshift supports dynamic data masking, which can be implemented with minimal effort. This solution ensures that the data scientistcan access the required information while sensitive data remains protected, meeting the requirements efficiently and with the least implementation effort.

NEW QUESTION # 35
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 a CloudWatch alarm to send the email message.
  • B. Log the metrics from the Lambda function to Amazon CloudWatch. Configure an Amazon CloudFront rule to send the email message.
  • C. Log the metrics from the Lambda function to AWS CloudTrail. Configure a CloudTrail trail to send the email message.
  • D. Log the metrics from the Lambda function to Amazon CloudFront. Configure an Amazon CloudWatch alarm to send the email message.
Answer: A

NEW QUESTION # 36
A company is planning to create an internal-only chat interface to help employees handle customer queries. Currently, the employees need to refer to a massive knowledge base of internal documents to address customer issues. The new solution must be serverless. Which combination of steps will meet these requirements?
  • A. Use an Amazon S3 bucket to store vector database dumps and embeddings.
  • B. Use AWS Lambda functions with Amazon API Gateway to invoke the model API.
  • C. Use Amazon RDS for MySQL to store vector database dumps and embeddings.
  • D. Set up Amazon Bedrock with the Anthropic Claude foundation model.
  • E. Use Amazon EC2 instances with Amazon API Gateway to invoke the model API.
  • F. Set up Amazon SageMaker JumpStart with the Llama foundation model.
Answer: A,B,D
Explanation:
To build a serverless internal chat interface, you can use Amazon Bedrock with a foundation model like Claude, invoke the model API through AWS Lambda with Amazon API Gateway, and store embeddings in a vector database format using Amazon S3. This avoids server management, ensures scalability, and leverages serverless components end-to-end.

NEW QUESTION # 37
A company is building a deep learning model on Amazon SageMaker. The company uses a large amount of data as the training dataset. The company needs to optimize the model's hyperparameters to minimize the loss function on the validation dataset.
Which hyperparameter tuning strategy will accomplish this goal with the LEAST computation time?
  • A. Hyperband
  • B. Random search
  • C. Grid search
  • D. Bayesian optimization
Answer: A

NEW QUESTION # 38
An ML engineer is preparing a dataset that contains medical records to train an ML model to predict the likelihood of patients developing diseases.
The dataset contains columns for patient ID, age, medical conditions, test results, and a "Disease" target column.
How should the ML engineer configure the data to train the model?
  • A. Remove the medical conditions and test results columns.
  • B. Remove the patient ID column.
  • C. Remove the "Disease" target column.
  • D. Remove the age column.
Answer: B
Explanation:
Patient ID is a unique identifier and does not contain predictive information. Including it can cause the model to overfit by memorizing records rather than learning meaningful patterns.
AWS ML best practices recommend removing identifiers that are not causally related to the target variable.
Age, medical conditions, and test results are clinically relevant features and should be retained. The target column must remain for supervised learning.
Therefore, Option A is the correct and AWS-aligned choice.

NEW QUESTION # 39
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Posted at yesterday 06:24        Only Author  2#
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