Amazon AWS Certified Data Engineer - Associate (DEA-C01) : Data-Engineer-Associate

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

1. A financial company wants to use Amazon Athena to run on-demand SQL queries on a petabyte-scale dataset to support a business intelligence (BI) application. An AWS Glue job that runs during non-business hours updates the dataset once every day. The BI application has a standard data refresh frequency of 1 hour to comply with company policies.
A data engineer wants to cost optimize the company ' s use of Amazon Athena without adding any additional infrastructure costs.
Which solution will meet these requirements with the LEAST operational overhead?

A) Configure an Amazon S3 Lifecycle policy to move data to the S3 Glacier Deep Archive storage class after 1 day
B) Add an Amazon ElastiCache cluster between the Bl application and Athena.
C) Use the query result reuse feature of Amazon Athena for the SQL queries.
D) Change the format of the files that are in the dataset to Apache Parquet.

2. A company is uploading log files from on-premises servers to an Amazon S3 bucket. The company needs to validate that the logs from the on-premises servers are the same as the logs that are stored in the S3 bucket.
Which solution will meet this requirement?

A) Use the AWS SDK to automatically compute CRC32 checksums during the upload. Store the checksums in S3 object metadata.
B) Create an AWS Lambda function to calculate SHA-256 checksums. Store the results in a separate metadata table. Validate the logs after the upload.
C) Enable S3 Object Lock in compliance mode on the S3 bucket. Upload the objects to the bucket.
D) After uploading the objects to the S3 bucket, enable S3 Object Lock in governance mode on the S3 objects.

3. A car sales company maintains data about cars that are listed for sale in an area. The company receives data about new car listings from vendors who upload the data daily as compressed files into Amazon S3. The compressed files are up to 5 KB in size. The company wants to see the most up-to-date listings as soon as the data is uploaded to Amazon S3.
A data engineer must automate and orchestrate the data processing workflow of the listings to feed a dashboard. The data engineer must also provide the ability to perform one-time queries and analytical reporting. The query solution must be scalable.
Which solution will meet these requirements MOST cost-effectively?

A) Use AWS Glue to process incoming data. Use AWS Lambda and S3 Event Notifications to orchestrate workflows. Use Amazon Athena for one-time queries and analytical reporting. Use Amazon QuickSight for the dashboard.
B) Use AWS Glue to process incoming data. Use AWS Step Functions to orchestrate workflows. Use Amazon Redshift Spectrum for one-time queries and analytical reporting. Use OpenSearch Dashboards in Amazon OpenSearch Service for the dashboard.
C) Use an Amazon EMR cluster to process incoming data. Use AWS Step Functions to orchestrate workflows. Use Apache Hive for one-time queries and analytical reporting. Use Amazon OpenSearch Service to bulk ingest the data into compute optimized instances. Use OpenSearch Dashboards in OpenSearch Service for the dashboard.
D) Use a provisioned Amazon EMR cluster to process incoming data. Use AWS Step Functions to orchestrate workflows. Use Amazon Athena for one-time queries and analytical reporting. Use Amazon QuickSight for the dashboard.

4. A data engineer needs to schedule a workflow that runs a set of AWS Glue jobs every day. The data engineer does not require the Glue jobs to run or finish at a specific time.
Which solution will run the Glue jobs in the MOST cost-effective way?

A) Choose the STANDARD execution class in the Glue job properties.
B) Choose the latest version in the GlueVersion field in the Glue job properties.
C) Use the Spot Instance type in Glue job properties.
D) Choose the FLEX execution class in the Glue job properties.

5. A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue.
The data engineer ' s original query is as follows:
SELECT product_name, sum(sales_amount)
FROM sales_data
WHERE year = 2023
GROUP BY product_name
How should the data engineer modify the Athena query to meet these requirements?

A) Add HAVING sumfsales amount) > 0 after the GROUP BY clause.
B) Change WHERE year = 2023 to WHERE extractlyear FROM sales data) = 2023.
C) Replace sum(sales amount) with count(*J for the aggregation.
D) Remove the GROUP BY clause

Solutions: