Free Practice Questions for Amazon Web Services Data-Engineer-Associate Exam

Option A is correct because Amazon Redshift supports the EXCLUDE clause in the SELECT list for exactly this use case: returning all columns from a wide table except a small set of unwanted columns. AWS documentation states that “EXCLUDE column_list names the columns that are excluded from the query results” and notes that the option is helpful when only a subset of columns must be excluded from a wide table. That matches this question precisely because the table contains 100 columns and only company_id and unique_system_id need to be omitted.

Option B is invalid SQL syntax in Amazon Redshift because NOT IN is used in predicates, not in the select list to remove columns. Option C is also incorrect because EXCEPT in Redshift is a set operator used between query result sets, not a select-list column exclusion feature. AWS documents EXCEPT alongside UNION and INTERSECT as result-set comparison operators, not as a way to omit columns from SELECT *. Option D is not valid Redshift SQL syntax for column selection. Therefore, the correct and documented Redshift statement is SELECT * EXCLUDE company_id, unique_system_id FROM orders.complete_orders_history;

To build an enterprise data catalog with metadata for storage formats, the easiest and most efficient solution is using an AWS Glue crawler. The Glue crawler can scan Amazon S3 buckets and Amazon RDS databases to automatically create a data catalog that includes metadata such as the schema and storage format (e.g., CSV, Parquet, etc.). By using AWS Glue crawler classifiers, you can configure the crawler to recognize the format of the data and store this information directly in the catalog.

Option B: Use an AWS Glue crawler to build a data catalog. Use AWS Glue crawler classifiers to recognize the format of data and store the format in the catalog.This option meets the requirements with the least effort because Glue crawlers automate the discovery and cataloging of data from multiple sources, including S3 and RDS, while recognizing various file formats via classifiers.

Other options (A, C, D) involve additional manual steps, like having data stewards inspect the data, or using services like Amazon Macie that focus more on sensitive data detection rather than format cataloging.

Option C is correct because AWS Glue workflows are designed to orchestrate multiple ETL jobs, crawlers, and triggers with dependency management and a visual workflow graph. AWS documentation states that Glue workflows can create and visualize complex ETL activities involving multiple jobs and triggers, and that triggers can be configured so that a job starts only when multiple watched jobs satisfy specified completion conditions. AWS also states that a conditional trigger can fire when any or all watched jobs complete with the desired status. That directly matches the requirement to run the first two processes concurrently and begin the third process only after both finish.

This is also the least operational overhead option because the whole workflow stays inside AWS Glue, which already fits the ETL conversion use case from CSV to Parquet on S3 with catalog integration. Option A would work but adds MWAA operational complexity. Option B is more custom and infrastructure-heavy. Option D using Lambda is not ideal for long-running, memory-intensive ETL steps. Therefore, AWS Glue workflows is the most direct and exam-aligned answer.

The correct answer is A because the requirement specifically says that traffic between Lambda and the Amazon Redshift Data API must remain on the AWS network. AWS documentation states that you can call the Redshift Data API with Amazon VPC endpoints and that this keeps traffic between applications in the VPC and the Data API in the AWS network without public IP addresses. The Data API also removes the need to manage database drivers, persistent connections, and network configuration from Lambda. Creating a VPC endpoint for Lambda is not how Lambda privately reaches Redshift Data API. ODBC and JDBC drivers introduce connection management and do not address the stated Data API traffic requirement. Therefore, Data API plus the Redshift Data API VPC endpoint is correct.

Publishing AWS Glue data quality scores to Amazon DataZone requires creating a DQDL ruleset, scheduling it to run regularly, and then linking the corresponding AWS Glue table as a data source in the DataZone project. The setup ensures that data quality scores from Glue are correctly published and accessible within Amazon DataZone:

“You can define DQDL rulesets for Glue tables and publish the data quality results to DataZone when the project is configured with an AWS Glue data source and the rulesets are scheduled.”

– Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

Option C follows the expected flow without unnecessary complexity and aligns perfectly with the integration flow supported by AWS.

AWS Glue streaming jobs are purpose-built for low-latency, near real-time ETL pipelines.

Glue Studio allows visual job authoring and supports complex transformations, including joins with Amazon RDS databases, through JDBC connectors.

Data can be written directly to S3 in Apache Parquet format using built-in AWS Glue support.

Options B and C are more suitable for batch or development/testing. Option D is for custom batch jobs, not real-time processing.

“You can use AWS Glue streaming jobs with Glue Studio to process streaming data, perform transformations, join with RDS data sources, and write results in formats like Parquet to S3.”

Option B is the best solution to meet the requirements with the least operational overhead because S3 Select is a feature that allows you to retrieve only a subset of data from an S3 object by using simple SQL expressions. S3 Select works on objects stored in CSV, JSON, or Parquet format. By using S3 Select, you can avoid the need to download and process the entire S3 object, which reduces the amount of data transferred and the computation time. S3 Select is also easy to use and does not require any additional services or resources.

Option A is not a good solution because it involves writing custom code and configuring an AWS Lambda function to load data from the S3 bucket into a pandas dataframe and query the required column. This option adds complexity and latency to the data retrieval process and requires additional resources and configuration. Moreover, AWS Lambda has limitations on the execution time, memory, and concurrency, which may affect the performance and reliability of the data retrieval process.

Option C is not a good solution because it involves creating and running an AWS Glue DataBrew project to consume the S3 objects and query the required column. AWS Glue DataBrew is a visual data preparation tool that allows you to clean, normalize, and transform data without writing code. However, in this scenario, the data is already in Parquet format, which is a columnar storage format that is optimized for analytics. Therefore, there is no need to use AWS Glue DataBrew to prepare the data. Moreover, AWS Glue DataBrew adds extra time and cost to the data retrieval process and requires additional resources and configuration.

Option D is not a good solution because it involves running an AWS Glue crawler on the S3 objects and using a SQL SELECT statement in Amazon Athena to query the required column. An AWS Glue crawler is a service that can scan data sources and create metadata tables in the AWS Glue Data Catalog. The Data Catalog is a central repository that stores information about the data sources, such as schema, format, and location. Amazon Athena is a serverless interactive query service that allows you to analyze data in S3 using standard SQL. However, in this scenario, the schema and format of the data are already known and fixed, so there is no need to run a crawler to discover them. Moreover, running a crawler and using Amazon Athena adds extra time and cost to the data retrieval process and requires additional services and configuration.

AWS Certified Data Engineer - Associate DEA-C01 Complete Study Guide

S3 Select and Glacier Select - Amazon Simple Storage Service

AWS Lambda - FAQs

What Is AWS Glue DataBrew? - AWS Glue DataBrew

Populating the AWS Glue Data Catalog - AWS Glue

What is Amazon Athena? - Amazon Athena

The company ' s Apache Spark ETL job on Amazon EMR uses high CPU but low memory, meaning that compute-optimized EC2 instances would be the most cost-effective choice. These instances are designed for high-performance compute applications, where CPU usage is high, but memory needs are minimal, which is exactly the case here.

Compute Optimized Instances:

Compute-optimized instances, such as the C5 series, provide a higher ratio of CPU to memory, which is more suitable for jobs with high CPU usage and relatively low memory consumption.

Switching from general-purpose EC2 instances to compute-optimized instances can reduce costs while improving performance, as these instances are optimized for workloads like Spark jobs that perform a lot of computation.

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