FREE PRACTICE QUESTIONS
Data Engineer Associate
Are you ready to sit your AWS Data Engineer Associate exam? Test your knowledge with these free practice questions. To give you a taste of our popular AWS Data Engineer practice exams, we have compiled these free AWS quiz questions. No sign-up required. Simply click on the AWS sample questions below to reveal the right answers along with explanations and reference links. If you’re looking for more free AWS practice questions, sign-up for our free AWS practice test for the AWS Certified Data Engineer Associate.
Click on the AWS Data Engineer sample questions below to reveal the correct answers and explanations with reference links.
A: Use Amazon Redshift Spectrum to execute SQL queries on CSV files in S3.
B: Use AWS Data Pipeline to transfer CSV data from S3 to Amazon RDS for querying.
C: Use Amazon Athena to run SQL queries directly on the CSV files stored in S3.
D: Use Amazon EMR with a Hive metastore to query the CSV files in the S3 bucket.
The correct answer is C. “Use Amazon Athena to run SQL queries directly on the CSV files stored in S3.“
Amazon Athena is an interactive query service that enables users to analyze data directly in Amazon S3 using standard SQL. It is perfectly suited for the company’s requirement to run SQL queries on CSV files stored in S3.
Athena is serverless, so there’s no infrastructure to manage, and it works directly with data in various formats, including CSV, stored in S3. This makes it an ideal choice for querying large datasets without the need for data loading or transformation, simplifying the process and reducing operational overhead.
- Answer A “Use Amazon Redshift Spectrum to execute SQL queries on CSV files in S3” is incorrect. While Amazon Redshift Spectrum allows querying data in S3, it is part of the Redshift data warehousing service and requires managing a Redshift cluster. This might be more complex and costlier than necessary for directly querying CSV files, as required by the company.
- Answer B “Use AWS Data Pipeline to transfer CSV data from S3 to Amazon RDS for querying” is incorrect. AWS Data Pipeline is a web service for orchestrating data movement and transformations, but it requires transferring data to a database service like Amazon RDS. This adds an unnecessary step of moving data from S3 to RDS, which is not required with services like Athena that can query data directly in S3.
- Answer D “Use Amazon EMR with a Hive metastore to query the CSV files in the S3 bucket” is incorrect. Amazon EMR can be used for processing large datasets and can query data in S3. However, setting up EMR and configuring a Hive metastore is more complex and resource-intensive compared to using Athena. For direct SQL querying of data in S3, Athena is more straightforward and cost-effective.
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A:Use AWS Glue to convert the .csv data to Apache Parquet and partition it by timestamp.
B: Run an Athena CTAS query to convert the data to Parquet format with Snappy compression, partitioned by timestamp.
C: Configure an EMR Spark job to transform the .csv files into Parquet, partitioned by the creation timestamp.
D: Set up a daily Lambda function to convert and partition the .csv data into Parquet format within S3.
The correct answer is B. “Run an Athena CTAS query to convert the data to Parquet format with Snappy compression, partitioned by timestamp“.
Amazon Athena’s CTAS feature enables the conversion of data into columnar formats like Parquet, which is optimized for analytics. Parquet format, combined with Snappy compression, improves performance, and reduces costs by minimizing storage use and speeding up query times.
Partitioning the data by timestamp ensures efficient data management and retrieval, aligning with the need for daily removal of outdated data. This serverless solution does not require managing any infrastructure, making it cost-effective for transforming and optimizing data directly within S3.
- Answer A “Use AWS Glue to convert the .csv data to Apache Parquet and partition it by timestamp” is incorrect. AWS Glue is a fully managed ETL service that could perform the transformation. However, for a straightforward conversion and partitioning task, AWS Glue might introduce more complexity and cost compared to the simplicity and serverless nature of an Athena CTAS query.
- Answer C “Configure an EMR Spark job to transform the .csv files into Parquet, partitioned by the creation timestamp” is incorrect. Amazon EMR is a managed cluster platform that runs big data frameworks like Apache Spark. While EMR can handle the task, it is typically more cost-effective for large-scale, complex processing jobs. The overhead of setting up and managing an EMR cluster is not necessary for this scenario, making Athena a more cost-effective choice.
- Answer D “Set up a daily Lambda function to convert and partition the .csv data into Parquet format within S3” is incorrect. AWS Lambda allows running code in response to events, such as new data arrival. However, for data transformation tasks, particularly for large datasets, Lambda may face limitations in terms of execution time and memory. Athena’s CTAS is a more suitable tool for large-scale data transformation without these limitations.
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A: Manage access through S3 bucket policies and IAM roles for row and column-level security.
B: Deploy Apache Ranger on Amazon EMR for granular access control and utilize Amazon Redshift for querying.
C: Use Redshift security groups and views for row and column-level permissions, querying with Athena and Redshift Spectrum.
D: Use AWS Lake Formation to define fine-grained data access policies and facilitate queries through supported AWS services.
The correct answer is D: “Use AWS Lake Formation to define fine-grained data access policies and facilitate queries through supported AWS services“.
AWS Lake Formation simplifies and centralizes the setup of a secure data lake in AWS. It provides granular access control to data stored in Amazon S3, allowing organizations to define who has access to specific rows and columns within their datasets.
Lake Formation integrates seamlessly with Amazon Athena, Amazon Redshift Spectrum, and Apache Hive on Amazon EMR, providing the least operational overhead while meeting the organization’s access control requirements.
A. “Manage access through S3 bucket policies and IAM roles for row and column-level security” is incorrect. While S3 bucket policies and IAM roles can provide access control, they do not offer row-level and column-level security natively. This approach would require additional management and does not integrate as seamlessly with the querying services for granular permissions as AWS Lake Formation does.
B. “Deploy Apache Ranger on Amazon EMR for granular access control and utilize Amazon Redshift for querying” is incorrect. Apache Ranger can provide fine-grained access control, but it is typically used within the Hadoop ecosystem and requires additional setup and management when used on Amazon EMR. This approach would also not be as integrated with Amazon Redshift for querying without further configuration, leading to a higher operational overhead compared to using AWS Lake Formation.
C. “Use Redshift security groups and views for row and column-level permissions, querying with Athena and Redshift Spectrum” is incorrect. Amazon Redshift security groups and views can control access to data, but they are specific to the Redshift environment. This method would not provide the same level of granular access control across other services like Amazon Athena and would not apply to data stored in Amazon S3, which is the intended storage for the data lake. Redshift is also not typically used as the primary storage for a data lake due to cost and scalability considerations compared to Amazon S3.
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Take this Free AWS Practice Exam
This free AWS practice exam for the AWS Data Engineer Associate consists of 20 questions that include detailed answers and explanations. You’ll be presented with a mix of questions on the core data engineering topics covered in the DEA-C01 exam.
Please note that unlike our exam simulator, this free AWS practice test is not timed – so you can take as much time as required to answer each question. At the end of the AWS Data Engineer practice exam, you get to review your answers and find detailed explanations why each answer is wrong or right along with reference links for each question. This will help you identify your strength and weaknesses.
How to best prepare for your AWS Data Engineer Associate Exam
Practice makes perfect! To maximize your chances of success, enroll in our training for the AWS Data Engineer.
The AWS Data Engineer practice exam course consists of 6 practice tests with 25 questions each (total of 150 unique questions).
Our Practice Exams are delivered in 4 different modes:
(1) Exam Mode (timed): In exam mode, you’ll find 6 sets of practice exams that are timed and scored – reflecting the difficulty of the real AWS exam.
(2) Training Mode (not timed): When taking the practice exam in training mode, you’ll see the answers and explanations for every question after clicking “check”.
(3) Knowledge Reviews (Deep Dive): With our knowledge reviews, you get to dive deep with a series of questions that focus on a specific topic.
(4) Final Exam Simulator (timed and scored): The final exam simulator randomly selects 65 questions from our pool questions – mimicking the real AWS exam environment.
Sign up for our monthly or yearly plans to access our popular AWS Data Engineer training – simply the best way to ensure you pass your exam the first time with a great score.
AWS Certified Data Engineer Associate Exam
Here are the most important facts about the official AWS Certified Data Engineer Associate Exam (DEA-C01)
|AWS Certified Data Engineer Associate
|Multiple choice or multiple response
|Number of Questions:
|English, Japanese, Korean, Simplified Chinese
|Exam Delivery Format:
|Pearson VUE (testing center or online proctored exam)
|Official Exam Guide
|Download the Official Exam Guide