MLA-C01 Free Practice Test

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?

1. A. Configure dynamic data masking policies to control how sensitive data is shared with the data scientist at query time.
2. B. Create a materialized view with masking logic on top of the databas
3. C. Grant the necessary read permissions to the data scientist.
4. D. Unload the Amazon Redshift data to Amazon S3. Use Amazon Athena to create schema-on-read with masking logi
5. E. Share the view with the data scientist.
6. F. Unload the Amazon Redshift data to Amazon S3. Create an AWS Glue job to anonymize the dat
7. G. Share the dataset with the data scientist.

Correct Answer: A

A company has a Retrieval Augmented Generation (RAG) application that uses a vector database to store embeddings of documents. The company must migrate the application to AWS and must implement a solution that provides semantic search of text files. The company has already migrated the text repository to an Amazon S3 bucket.
Which solution will meet these requirements?

1. A. Use an AWS Batch job to process the files and generate embedding
2. B. Use AWS Glue to store the embedding
3. C. Use SQL queries to perform the semantic searches.
4. D. Use a custom Amazon SageMaker notebook to run a custom script to generate embedding
5. E. Use SageMaker Feature Store to store the embedding
6. F. Use SQL queries to perform the semantic searches.
7. G. Use the Amazon Kendra S3 connector to ingest the documents from the S3 bucket into Amazon Kendr
8. H. Query Amazon Kendra to perform the semantic searches.
9. I. Use an Amazon Textract asynchronous job to ingest the documents from the S3 bucke
10. J. Query Amazon Textract to perform the semantic searches.

Correct Answer: C

A company uses Amazon SageMaker Studio to develop an ML model. The company has a single SageMaker Studio domain. An ML engineer needs to implement a solution that provides an automated alert when SageMaker compute costs reach a specific threshold.
Which solution will meet these requirements?

1. A. Add resource tagging by editing the SageMaker user profile in the SageMaker domai
2. B. Configure AWS Cost Explorer to send an alert when the threshold is reached.
3. C. Add resource tagging by editing the SageMaker user profile in the SageMaker domai
4. D. Configure AWS Budgets to send an alert when the threshold is reached.
5. E. Add resource tagging by editing each user's IAM profil
6. F. Configure AWS Cost Explorer to send an alert when the threshold is reached.
7. G. Add resource tagging by editing each user's IAM profil
8. H. Configure AWS Budgets to send an alert when the threshold is reached.

Correct Answer: B

An ML engineer needs to use data with Amazon SageMaker Canvas to train an ML model. The data is stored in Amazon S3 and is complex in structure. The ML engineer must use a file format that minimizes processing time for the data.
Which file format will meet these requirements?

1. A. CSV files compressed with Snappy
2. B. JSON objects in JSONL format
3. C. JSON files compressed with gzip
4. D. Apache Parquet files

Correct Answer: D

An ML engineer needs to use an ML model to predict the price of apartments in a specific location.
Which metric should the ML engineer use to evaluate the model's performance?

1. A. Accuracy
2. B. Area Under the ROC Curve (AUC)
3. C. F1 score
4. D. Mean absolute error (MAE)

Correct Answer: D