Free Amazon Data-Engineer-Associate Exam Questions

Question 1

As a data engineer, you are optimizing query performance for a client's data warehouse solution. The client is dealing with large datasets and is particularly focused on enhancing the performance of join queries while minimizing data shuffling between nodes.

Which distribution style within their data warehouse solution, specifically Amazon Redshift, would be best suited for this purpose?

A : Adopting the EVEN distribution style in Amazon Redshift, which distributes the data uniformly across all slices and nodes. This may not provide the most optimized method for join queries as it doesn't ensure data localization for join columns.

B : Implementing the AUTO distribution style in Amazon Redshift, allowing the service to automatically manage the distribution of data across nodes based on the table size and query patterns, potentially improving join query performance.

C : Selecting the ALL distribution style in Amazon Redshift, which duplicates the entire table across every node. While this can increase query speed for small tables, it can become inefficient for larger datasets due to high storage usage and performance implications.

D : Utilizing the KEY distribution style in Amazon Redshift by assigning it to a frequently joined column, thereby ensuring that data corresponding to the key is colocated on the same node, which can significantly reduce the amount of data shuffling required for join queries.

Answer: D

Question 2

A Cloud Data Engineering Team at a data analytics firm is tasked with enhancing their data schema management. The team is dealing with an extensive collection of datasets stored in Amazon S3, with data arriving in varying formats. The requirement is to automate the adaptation of data schemas to reduce the need for manual updates, thereby streamlining analysts' access to the latest data for their complex queries.

Which steps should the team implement to establish a dynamic and automated schema management system that seamlessly accommodates schema evolution? (Select TWO)

A : Schedule AWS Glue crawlers to automatically detect and incorporate schema modifications into the AWS Glue Data Catalog, accommodating the diverse data formats found in S3 buckets.

B : Develop a bespoke schema tracking application that mandates manual recording and validation of schema changes by data anaysts, aiming for rigorous oversight.

C : Implement AWS Glue Schema Registry within their ETL pipelines to manage schema versioning automatically as data is ingested and transformed, maintaining a record of schema changes.

D : Deploy AWS Glue crawlers for schema detection and an AWS Lambda function for external schema change tracking, using a third-party system instead of AWS-native schema management tools.

E : Manually update te AWS Glue Data Catalog in response to schema change alerts from data ingestion pipelines, ensuring controlled, deliberate schema evolution.

Answer: A,C

Question 3

A company receives test results from testing facilities that are located around the world. The company storesthe test results in millions of 1 KB JSON files in an Amazon S3 bucket. A data engineer needs to process thefiles, convert them into Apache Parquet format, and load them into Amazon Redshift tables. The dataengineer uses AWS Glue to process the files, AWS Step Functions to orchestrate the processes, and AmazonEventBridge to schedule jobs.The company recently added more testing facilities. The time required to process files is increasing. The dataengineer must reduce the data processing time.Which solution will MOST reduce the data processing time?

A : Use AWS Lambda to group the raw input files into larger files. Write the larger files back to Amazon S3. Use AWS Glue to process the files. Load the files into the Amazon Redshift tables.

B : Use the AWS Glue dynamic frame file-grouping option to ingest the raw input files. Process the files. Load the files into the Amazon Redshift tables.

C : Use the Amazon Redshift COPY command to move the raw input files from Amazon S3 directly into the Amazon Redshift tables. Process the files in Amazon Redshift.

D : Use Amazon EMR instead of AWS Glue to group the raw input files. Process the files in Amazon EMR. Load the files into the Amazon Redshift tables.

Answer: B

Question 4

A data engineer needs to build an enterprise data catalog based on the company's Amazon S3 buckets andAmazon RDS databases. The data catalog must include storage format metadata for the data in the catalog.Which solution will meet these requirements with the LEAST effort?

A : Use an AWS Glue crawler to scan the S3 buckets and RDS databases and build a data catalog. Use data stewards to inspect the data and update the data catalog with the data format.

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.

C : Use Amazon Macie to build a data catalog and to identify sensitive data elements. Collect the data format information from Macie.

D : Use scripts to scan data elements and to assign data classifications based on the format of the data.

Answer: B

Question 5

A company wants to migrate an application and an on-premises Apache Kafka server to AWS. Theapplication processes incremental updates that an on-premises Oracle database sends to the Kafka server. Thecompany wants to use the replatform migration strategy instead of the refactor strategy.Which solution will meet these requirements with the LEAST management overhead?

A : Amazon Kinesis Data Streams

B : Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned cluster

C : Amazon Data Firehose

D : Amazon Managed Streaming for Apache Kafka (Amazon MSK) Serverless

Answer: D