AWS-Certified-Data-Engineer-Associate Free Practice Test

A company stores details about transactions in an Amazon S3 bucket. The company wants to log all writes to the S3 bucket into another S3 bucket that is in the same AWS Region.
Which solution will meet this requirement with the LEAST operational effort?

1. A. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda functio
2. B. Program the Lambda function to write the event to Amazon Kinesis Data Firehos
3. C. Configure Kinesis Data Firehose to write the event to the logs S3 bucket.
4. D. Create a trail of management events in AWS CloudTrai
5. E. Configure the trail to receive data from the transactions S3 bucke
6. F. Specify an empty prefix and write-only event
7. G. Specify the logs S3 bucket as the destination bucket.
8. H. Configure an S3 Event Notifications rule for all activities on the transactions S3 bucket to invoke an AWS Lambda functio
9. I. Program the Lambda function to write the events to the logs S3 bucket.
10. J. Create a trail of data events in AWS CloudTrai
11. K. Configure the trail to receive data from the transactions S3 bucke
12. L. Specify an empty prefix and write-only event
13. M. Specify the logs S3 bucket as the destination bucket.

Correct Answer: D
This solution meets the requirement of logging all writes to the S3 bucket into another S3 bucket with the least operational effort. AWS CloudTrail is a service that records the API calls made to AWS services, including Amazon S3. By creating a trail of data events, you can capture the details of the requests that are made to the transactions S3 bucket, such as the requester, the time, the IP address, and the response elements. By specifying an empty prefix and write-only events, you can filter the data events to only include the ones that write to the bucket. By specifying the logs S3 bucket as the destination bucket, you can store the CloudTrail logs in another S3 bucket that is in the same AWS Region. This solution does not require any additional coding or configuration, and it is more scalable and reliable than using S3 Event Notifications and Lambda functions. References:
✑ Logging Amazon S3 API calls using AWS CloudTrail
✑ Creating a trail for data events
✑ Enabling Amazon S3 server access logging

A company loads transaction data for each day into Amazon Redshift tables at the end of each day. The company wants to have the ability to track which tables have been loaded and which tables still need to be loaded.
A data engineer wants to store the load statuses of Redshift tables in an Amazon DynamoDB table. The data engineer creates an AWS Lambda function to publish the details of the load statuses to DynamoDB.
How should the data engineer invoke the Lambda function to write load statuses to the DynamoDB table?

1. A. Use a second Lambda function to invoke the first Lambda function based on Amazon CloudWatch events.
2. B. Use the Amazon Redshift Data API to publish an event to Amazon EventBridq
3. C. Configure an EventBridge rule to invoke the Lambda function.
4. D. Use the Amazon Redshift Data API to publish a message to an Amazon Simple Queue Service (Amazon SQS) queu
5. E. Configure the SQS queue to invoke the Lambda function.
6. F. Use a second Lambda function to invoke the first Lambda function based on AWS CloudTrail events.

Correct Answer: B
The Amazon Redshift Data API enables you to interact with your Amazon Redshift data warehouse in an easy and secure way. You can use the Data API to run SQL commands, such as loading data into tables, without requiring a persistent connection to the cluster. The Data API also integrates with Amazon EventBridge, which allows you to monitor the execution status of your SQL commands and trigger actions based on events. By using the Data API to publish an event to EventBridge, the data engineer can invoke the Lambda function that writes the load statuses to the DynamoDB table. This solution is scalable, reliable, and cost-effective. The other options are either not possible or not optimal. You cannot use a second Lambda function to invoke the first Lambda function based on CloudWatch or CloudTrail events, as these services do not capture the load status of Redshift tables. You can use the Data API to publish a message to an SQS queue, but this would require additional configuration and polling logic to invoke the Lambda function from the queue. This would also introduce additional latency and cost. References:
✑ Using the Amazon Redshift Data API
✑ Using Amazon EventBridge with Amazon Redshift
✑ AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 2: Data Store Management, Section 2.2: Amazon Redshift

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?

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

Correct Answer: B
The original query does not return results for all of the products because the year column in the sales_data table is not an integer, but a timestamp. Therefore, the WHERE clause does not filter the data correctly, and only returns the products that have a null value for the year column. To fix this, the data engineer should use the extract function to extract the year from the timestamp and compare it with 2023. This way, the querywill return the correct results for all of the products in the sales_data table. The other options are either incorrect or irrelevant, as they do not address the root cause of the issue. Replacing sum with count does not change the filtering condition, adding HAVING clause does not affect the grouping logic, and removing the GROUP BY clause does not solve the problem of missing products. References:
✑ Troubleshooting JSON queries - Amazon Athena (Section: JSON related errors)
✑ When I query a table in Amazon Athena, the TIMESTAMP result is empty (Section: Resolution)
✑ AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 7, page 197)

A company stores data in a data lake that is in Amazon S3. Some data that the company stores in the data lake contains personally identifiable information (PII). Multiple user groups need to access the raw data. The company must ensure that user groups can access only the PII that they require.
Which solution will meet these requirements with the LEAST effort?

1. A. Use Amazon Athena to query the dat
2. B. Set up AWS Lake Formation and create data filters to establish levels of access for the company's IAM role
3. C. Assign each user to the IAM role that matches the user's PII access requirements.
4. D. Use Amazon QuickSight to access the dat
5. E. Use column-level security features in QuickSight to limit the PII that users can retrieve from Amazon S3 by using Amazon Athen
6. F. Define QuickSight access levels based on the PII access requirements of the users.
7. G. Build a custom query builder UI that will run Athena queries in the background to access the dat
8. H. Create user groups in Amazon Cognit
9. I. Assign access levels to the user groups based on the PII access requirements of the users.
10. J. Create IAM roles that have different levels of granular acces
11. K. Assign the IAM roles to IAM user group
12. L. Use an identity-based policy to assign access levels to user groups at the column level.

Correct Answer: A
Amazon Athena is a serverless, interactive query service that enables you to analyze data in Amazon S3 using standard SQL. AWS Lake Formation is a service that helps you build, secure, and manage data lakes on AWS. You can use AWS Lake Formation to create data filters that define the level of access for different IAM roles based on the columns, rows, or tags of the data. By using Amazon Athena to query the data and AWS Lake Formation to create data filters, the company can meet the requirements of ensuring that user groups can access only the PII that they require with the least effort. The solution is to use Amazon Athena to query the data in the data lake that is in Amazon S3. Then, set up AWS Lake Formation and create data filters to establish levels of access for the company’s IAM roles. For example, a data filter can allow a user group to access only the columns that contain the PII that they need, such as name and email address, and deny access to the columns that contain the PII that they do not need, such as phone number and social security number. Finally, assign each user to the IAM role that matches the user’s PII access requirements. This way, the user groups can access the data in the data lake securely and efficiently. The other options are either not feasible or not optimal. Using Amazon QuickSight to access the data (option B) would require the company to pay for the QuickSight service and to configure the column-level security features for each user. Building a custom query builder UI that will run Athena queries in the background to access the data (option C) would require the company to develop and maintain the UI and to integrate it with Amazon Cognito. Creating IAM roles that have different levels of granular access (option D) would require the company to manage multiple IAM roles and policies and to ensure that they are aligned with the data schema. References:
✑ Amazon Athena
✑ AWS Lake Formation
✑ AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide, Chapter 4: Data Analysis and Visualization, Section 4.3: Amazon Athena

A company uses an Amazon QuickSight dashboard to monitor usage of one of the company's applications. The company uses AWS Glue jobs to process data for the dashboard. The company stores the data in a single Amazon S3 bucket. The company adds new data every day.
A data engineer discovers that dashboard queries are becoming slower over time. The data engineer determines that the root cause of the slowing queries is long-running AWS Glue jobs.
Which actions should the data engineer take to improve the performance of the AWS Glue jobs? (Choose two.)

1. A. Partition the data that is in the S3 bucke
2. B. Organize the data by year, month, and day.
3. C. Increase the AWS Glue instance size by scaling up the worker type.
4. D. Convert the AWS Glue schema to the DynamicFrame schema class.
5. E. Adjust AWS Glue job scheduling frequency so the jobs run half as many times each day.
6. F. Modify the 1AM role that grants access to AWS glue to grant access to all S3 features.

Correct Answer: AB
Partitioning the data in the S3 bucket can improve the performance of AWS Glue jobs by reducing the amount of data that needs to be scanned and processed. By organizingthe data by year, month, and day, the AWS Glue job can use partition pruning to filter out irrelevant data and only read the data that matches the query criteria. This can speed up the data processing and reduce the cost of running the AWS Glue job. Increasing the AWS Glue instance size by scaling up the worker type can also improve the performance of AWS Glue jobs by providing more memory and CPU resources for the Spark execution engine. This can help the AWS Glue job handle larger data sets and complex transformations more efficiently. The other options are either incorrect or irrelevant, as they do not affect the performance of the AWS Glue jobs. Converting the AWS Glue schema to the DynamicFrame schema class does not improve the performance, but rather provides additional functionality and flexibility for data manipulation. Adjusting the AWS Glue job scheduling frequency does not improve the performance, but rather reduces the frequency of data updates. Modifying the IAM role that grants access to AWS Glue does not improve the performance, but rather affects the security and permissions of the AWS Glue service. References:
✑ Optimising Glue Scripts for Efficient Data Processing: Part 1 (Section: Partitioning Data in S3)
✑ Best practices to optimize cost and performance for AWS Glue streaming ETL jobs (Section: Development tools)
✑ Monitoring with AWS Glue job run insights (Section: Requirements)
✑ AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide (Chapter 5, page 133)