Amazon Data-Engineer-Associate Practice Test [2026]

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Amazon AWS Certified Data Engineer - Associate (DEA-C01) Sample Questions (Q184-Q189):

NEW QUESTION # 184
A company uploads .csv files to an Amazon S3 bucket. The company's data platform team has set up an AWS Glue crawler to perform data discovery and to create the tables and schemas.
An AWS Glue job writes processed data from the tables to an Amazon Redshift database. The AWS Glue job handles column mapping and creates the Amazon Redshift tables in the Redshift database appropriately.
If the company reruns the AWS Glue job for any reason, duplicate records are introduced into the Amazon Redshift tables. The company needs a solution that will update the Redshift tables without duplicates.
Which solution will meet these requirements?

Answer: B

Explanation:
To avoid duplicate records in Amazon Redshift, the most effective solution is to perform the ETL in a way that first loads the data into a staging table and then uses SQL commands like MERGE or UPDATE to insert new records and update existing records without introducing duplicates.
* Using Staging Tables in Redshift:
* The AWS Glue job can write data to a staging table in Redshift. Once the data is loaded, SQL commands can be executed to compare the staging data with the target table and update or insert records appropriately. This ensures no duplicates are introduced during re-runs of the Glue job.


NEW QUESTION # 185
A company uses AWS Glue jobs to implement several data pipelines. The pipelines are critical to the company.
The company needs to implement a monitoring mechanism that will alert stakeholders if the pipelines fail.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: D

Explanation:
Creating an EventBridge rule that triggers a Lambda function on AWS Glue job failure events and then sends notifications via Amazon SNS is the most direct and operationally efficient method:
"Practice Quiz 10: A data engineer must monitor the data pipeline... Which solution will meet these requirements?
A). Inspect the job run monitoring section of the AWS Glue console.answer: A."
- Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf Although this reference directly supports using AWS Glue's monitoring features via EventBridge, it implies that solutions like A-which directly use EventBridge failure events for automation-are more optimal and less complex than constructing custom logs and metrics.


NEW QUESTION # 186
A data engineer is designing a new data lake architecture for a company. The data engineer plans to use Apache Iceberg tables and AWS Glue Data Catalog to achieve fast query performance and enhanced metadata handling. The data engineer needs to query historical data for trend analysis and optimize storage costs for a large volume of event data.
Which solution will meet these requirements with the LEAST development effort?

Answer: B

Explanation:
Amazon S3 Intelligent-Tiering is designed to optimize storage costs by automatically moving objects between access tiers based on access patterns. Since Apache Iceberg works with S3 storage, using Intelligent-Tiering provides cost-efficiency without the need for custom development or jobs.
* Option B improves performance but doesn't optimize cost automatically.
* Option C is not a real AWS Glue feature - Glue does not automatically optimize Iceberg storage.
* Option D requires custom development effort, which is contrary to the requirement.
"S3 Intelligent-Tiering is ideal for data lakes and analytics use cases that access data irregularly." Reference: AWS Documentation - S3 Intelligent-Tiering


NEW QUESTION # 187
A retail company uses an Amazon Redshift data warehouse and an Amazon S3 bucket. The company ingests retail order data into the S3 bucket every day.
The company stores all order data at a single path within the S3 bucket. The data has more than 100 columns. The company ingests the order data from a third-party application that generates more than 30 files in CSV format every day. Each CSV file is between 50 and 70 MB in size.
The company uses Amazon Redshift Spectrum to run queries that select sets of columns. Users aggregate metrics based on daily orders. Recently, users have reported that the performance of the queries has degraded. A data engineer must resolve the performance issues for the queries.
Which combination of steps will meet this requirement with LEAST developmental effort? (Select TWO.)

Answer: B,E

Explanation:
The performance issue in Amazon Redshift Spectrum queries arises due to the nature of CSV files, which are row-based storage formats. Spectrum is more optimized for columnar formats, which significantly improve performance by reducing the amount of data scanned. Also, partitioning data based on relevant columns like order date can further reduce the amount of data scanned, as queries can focus only on the necessary partitions.
A . Configure the third-party application to create the files in a columnar format:
Columnar formats (like Parquet or ORC) store data in a way that is optimized for analytical queries because they allow queries to scan only the columns required, rather than scanning all columns in a row-based format like CSV.
Amazon Redshift Spectrum works much more efficiently with columnar formats, reducing the amount of data that needs to be scanned, which improves query performance.
Reference:
C . Partition the order data in the S3 bucket based on order date:
Partitioning the data on columns like order date allows Redshift Spectrum to skip scanning unnecessary partitions, leading to improved query performance.
By organizing data into partitions, you minimize the number of files Spectrum has to read, further optimizing performance.
Alternatives Considered:
B (Develop an AWS Glue ETL job): While consolidating files can improve performance by reducing the number of small files (which can be inefficient to process), it adds additional ETL complexity. Switching to a columnar format (Option A) and partitioning (Option C) provides more significant performance improvements with less development effort.
D and E (JSON-related options): Using JSON format or the SUPER type in Redshift introduces complexity and isn't as efficient as the proposed solutions, especially since JSON is not a columnar format.
Amazon Redshift Spectrum Documentation
Columnar Formats and Data Partitioning in S3


NEW QUESTION # 188
A data engineer needs to make tabular data available in an Amazon S3-based data lake. Users must be able to query the data by using SQL queries in Amazon Redshift, Amazon Athena, and Amazon EMR. The data is updated daily. The data engineer must ensure that updates and deletions are reflected in the data lake.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: D

Explanation:
Comprehensive and Detailed Explanation (150-250 words)
Apache Iceberg is a table format designed for large-scale data lakes that supports ACID transactions, schema evolution, time travel, and row-level updates and deletes. Using S3 Tables with Apache Iceberg provides a fully managed experience that integrates natively with Amazon Athena, Amazon Redshift, and Amazon EMR.
By using AWS Glue with the Iceberg catalog, the data engineer can perform daily updates and deletions without managing Spark clusters, compaction scheduling, or metadata cleanup manually. Iceberg handles snapshots, file pruning, and unreferenced file removal automatically, significantly reducing operational overhead.
Apache Hudi requires Amazon EMR clusters, Spark jobs, and manual compaction orchestration, increasing complexity. The Parquet-only approaches in options C and D do not support updates or deletes efficiently and would require full rewrites of datasets, which is not scalable.
Therefore, using S3 Tables with Apache Iceberg provides the most efficient, scalable, and low-maintenance solution that satisfies all query and update requirements.


NEW QUESTION # 189
......

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