FREE AMAZON DATA-ENGINEER-ASSOCIATE QUESTIONS

Free Amazon Data-Engineer-Associate Questions

Free Amazon Data-Engineer-Associate Questions

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q65-Q70):

NEW QUESTION # 65
A data engineering team is using an Amazon Redshift data warehouse for operational reporting. The team wants to prevent performance issues that might result from long- running queries. A data engineer must choose a system table in Amazon Redshift to record anomalies when a query optimizer identifies conditions that might indicate performance issues.
Which table views should the data engineer use to meet this requirement?

  • A. STL USAGE CONTROL
  • B. STL ALERT EVENT LOG
  • C. STL PLAN INFO
  • D. STL QUERY METRICS

Answer: B

Explanation:
The STL ALERT EVENT LOG table view records anomalies when the query optimizer identifies conditions that might indicate performance issues. These conditions include skewed data distribution, missing statistics, nested loop joins, and broadcasted data. The STL ALERT EVENT LOG table view can help the data engineer to identify and troubleshoot the root causes of performance issues and optimize the query execution plan. The other table views are not relevant for this requirement. STL USAGE CONTROL records the usage limits and quotas for Amazon Redshift resources. STL QUERY METRICS records the execution time and resource consumption of queries. STL PLAN INFO records the query execution plan and the steps involved in each query. References:
STL ALERT EVENT LOG
System Tables and Views
AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide


NEW QUESTION # 66
A company is planning to upgrade its Amazon Elastic Block Store (Amazon EBS) General Purpose SSD storage from gp2 to gp3. The company wants to prevent any interruptions in its Amazon EC2 instances that will cause data loss during the migration to the upgraded storage.
Which solution will meet these requirements with the LEAST operational overhead?

  • A. Create new gp3 volumes. Gradually transfer the data to the new gp3 volumes. When the transfer is complete, mount the new gp3 volumes to the EC2 instances to replace the gp2 volumes.
  • B. Use AWS DataSync to create new gp3 volumes. Transfer the data from the original gp2 volumes to the new gp3 volumes.
  • C. Change the volume type of the existing gp2 volumes to gp3. Enter new values for volume size, IOPS, and throughput.
  • D. Create snapshots of the gp2 volumes. Create new gp3 volumes from the snapshots. Attach the new gp3 volumes to the EC2 instances.

Answer: C

Explanation:
Changing the volume type of the existing gp2 volumes to gp3 is the easiest and fastest way to migrate to the new storage type without any downtime or data loss. You can use the AWS Management Console, the AWS CLI, or the Amazon EC2 API to modify the volume type, size, IOPS, and throughput of your gp2 volumes. The modification takes effect immediately, and you can monitor the progress of the modification using CloudWatch. The other options are either more complex or require additional steps, such as creating snapshots, transferring data, or attaching new volumes, which can increase the operational overhead and the risk of errors. Reference:
Migrating Amazon EBS volumes from gp2 to gp3 and save up to 20% on costs (Section: How to migrate from gp2 to gp3) Switching from gp2 Volumes to gp3 Volumes to Lower AWS EBS Costs (Section: How to Switch from GP2 Volumes to GP3 Volumes) Modifying the volume type, IOPS, or size of an EBS volume - Amazon Elastic Compute Cloud (Section: Modifying the volume type)


NEW QUESTION # 67
A data engineer configured an AWS Glue Data Catalog for data that is stored in Amazon S3 buckets. The data engineer needs to configure the Data Catalog to receive incremental updates.
The data engineer sets up event notifications for the S3 bucket and creates an Amazon Simple Queue Service (Amazon SQS) queue to receive the S3 events.
Which combination of steps should the data engineer take to meet these requirements with LEAST operational overhead? (Select TWO.)

  • A. Use an AWS Lambda function to directly update the Data Catalog based on S3 events that the SQS queue receives.
  • B. Create an S3 event-based AWS Glue crawler to consume events from the SQS queue.
  • C. Use AWS Step Functions to orchestrate the process of updating the Data Catalog based on 53 events that the SQS queue receives.
  • D. Manually initiate the AWS Glue crawler to perform updates to the Data Catalog when there is a change in the S3 bucket.
  • E. Define a time-based schedule to run the AWS Glue crawler, and perform incremental updates to the Data Catalog.

Answer: A,B

Explanation:
The requirement is to update the AWS Glue Data Catalog incrementally based on S3 events. Using an S3 event-based approach is the most automated and operationally efficient solution.
* A. Create an S3 event-based AWS Glue crawler:
* An event-based Glue crawler can automatically update the Data Catalog when new data arrives in the S3 bucket. This ensures incremental updates with minimal operational overhead.


NEW QUESTION # 68
A retail company is using an Amazon Redshift cluster to support real-time inventory management. The company has deployed an ML model on a real-time endpoint in Amazon SageMaker.
The company wants to make real-time inventory recommendations. The company also wants to make predictions about future inventory needs.
Which solutions will meet these requirements? (Select TWO.)

  • A. Use SQL to invoke a remote SageMaker endpoint for prediction.
  • B. Use Amazon Redshift as a file storage system to archive old inventory management reports.
  • C. Use SageMaker Autopilot to create inventory management dashboards in Amazon Redshift.
  • D. Use Amazon Redshift ML to generate inventory recommendations.
  • E. Use Amazon Redshift ML to schedule regular data exports for offline model training.

Answer: A,D

Explanation:
The company needs to use machine learning models for real-time inventory recommendations and future inventory predictions while leveraging both Amazon Redshift and Amazon SageMaker.
* Option A: Use Amazon Redshift ML to generate inventory recommendations.Amazon Redshift ML allows you to build, train, and deploy machine learning models directly from Redshift using SQL statements. It integrates with SageMaker to train models and run inference. This feature is useful for generating inventory recommendations directly from the data stored in Redshift.
* Option B: Use SQL to invoke a remote SageMaker endpoint for prediction.You can use SQL in Redshift to call a SageMaker endpoint for real-time inference. By invoking a SageMaker endpoint from within Redshift, the company can get real-time predictions on inventory, allowing for integration between the data warehouse and the machine learning model hosted in SageMaker.
* Option C (offline model training) and Option D (creating dashboards with SageMaker Autopilot) are not relevant to the real-time prediction and recommendation requirements.
* Option E (archiving inventory reports in Redshift) is not related to making predictions or recommendations.
References:
* Amazon Redshift ML Documentation
* Invoking SageMaker Endpoints from SQL


NEW QUESTION # 69
A data engineer is building an automated extract, transform, and load (ETL) ingestion pipeline by using AWS Glue. The pipeline ingests compressed files that are in an Amazon S3 bucket. The ingestion pipeline must support incremental data processing.
Which AWS Glue feature should the data engineer use to meet this requirement?

  • A. Job bookmarks
  • B. Classifiers
  • C. Triggers
  • D. Workflows

Answer: A

Explanation:
Problem Analysis:
The pipeline processes compressed files in S3 and must support incremental data processing.
AWS Glue features must facilitate tracking progress to avoid reprocessing the same data.
Key Considerations:
Incremental data processing requires tracking which files or partitions have already been processed.
The solution must be automated and efficient for large-scale ETL jobs.
Solution Analysis:
Option A: Workflows
Workflows organize and orchestrate multiple Glue jobs but do not track progress for incremental data processing.
Option B: Triggers
Triggers initiate Glue jobs based on a schedule or events but do not track which data has been processed.
Option C: Job Bookmarks
Job bookmarks track the state of the data that has been processed, enabling incremental processing.
Automatically skip files or partitions that were previously processed in Glue jobs.
Option D: Classifiers
Classifiers determine the schema of incoming data but do not handle incremental processing.
Final Recommendation:
Job bookmarks are specifically designed to enable incremental data processing in AWS Glue ETL pipelines.
Reference:
AWS Glue Job Bookmarks Documentation
AWS Glue ETL Features


NEW QUESTION # 70
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