The AWS Certified Data Engineer - Associate (DEA-C01) exam validates your ability to design, build, and manage data solutions on Amazon Web Services. This certification is ideal for professionals who work with data pipelines, storage systems, and analytics workflows. The exam tests both theoretical knowledge and practical decision-making across four core domains. This guide provides a structured overview of the DEA-C01 syllabus, question formats, and effective study strategies to help you prepare confidently.
Use this topic map to guide your study for Amazon DEA-C01 (AWS Certified Data Engineer - Associate) within the AWS Certified Data Engineer Associate path.
The DEA-C01 exam uses multiple question types to assess both conceptual understanding and applied reasoning. Questions progress in difficulty and reflect real-world data engineering scenarios.
Questions emphasize decision-making under constraints and connecting concepts across ingestion, storage, operations, and security domains.
Effective preparation requires mapping exam topics to a structured study plan and practicing with realistic questions. Allocate study time proportionally to domain weight and your current skill gaps. Regular review and hands-on practice with AWS services reinforce learning and build confidence.
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Data Ingestion and Transformation and Data Store Management typically account for the largest portion of exam questions. However, Data Operations and Support and Data Security and Governance are equally important for building complete data engineering competency. Review the official exam guide to confirm current domain weightings and allocate study time accordingly.
In practice, these domains form an integrated workflow: you ingest and transform data (domain 1), store it in appropriate systems (domain 2), monitor and maintain pipelines (domain 3), and enforce access and compliance controls (domain 4). Understanding these connections helps you make better design decisions and recognize how choices in one domain affect others.
Work with AWS services that appear frequently in the exam: AWS Glue for ETL, Amazon S3 for storage, Amazon RDS or Redshift for databases, and AWS Lake Formation for data governance. Create a simple end-to-end pipeline, configure encryption and access policies, and monitor it with CloudWatch. This practical experience builds confidence and deepens understanding beyond memorization.
Candidates often misunderstand service trade-offs (cost vs. performance vs. complexity), overlook security implications of design choices, and fail to consider operational aspects like monitoring and troubleshooting. Read scenario questions carefully, identify all constraints, and think through downstream effects before selecting an answer. Reviewing incorrect answers from practice tests helps you recognize these patterns.
Review your weakest topic areas using practice questions and explanations rather than re-reading study materials. Take a full-length timed practice test to assess readiness and identify any remaining gaps. In the days before the exam, focus on sleep, light review of key concepts, and building mental confidence rather than cramming new material.
Given the table sales which has a clustering key of column CLOSED_DATE which table function will return the average clustering depth for the SALES_REPRESENTATIVE column for the North American region?
A)
B)
C)
D)
The table function SYSTEM$CLUSTERING_DEPTH returns the average clustering depth for a specified column or set of columns in a table. The function takes two arguments: the table name and the column name(s). In this case, the table name is sales and the column name is SALES_REPRESENTATIVE. The function also supports a WHERE clause to filter the rows for which the clustering depth is calculated. In this case, the WHERE clause is REGION = 'North America'. Therefore, the function call in Option B will return the desired result.
A Data Engineer is implementing a near real-time ingestion pipeline to toad data into Snowflake using the Snowflake Kafka connector. There will be three Kafka topics created.
......snowflake objects are created automatically when the Kafka connector starts? (Select THREE)
The Snowflake objects that are created automatically when the Kafka connector starts are tables, pipes, and internal stages. The Kafka connector will create one table, one pipe, and one internal stage for each Kafka topic that is configured in the connector properties. The table will store the data from the Kafka topic, the pipe will load the data from the stage to the table using COPY statements, and the internal stage will store the files that are produced by the Kafka connector using PUT commands. The other options are not Snowflake objects that are created automatically when the Kafka connector starts. Option B, tasks, are objects that can execute SQL statements on a schedule without requiring a warehouse. Option E, external stages, are objects that can reference locations outside of Snowflake, such as cloud storage services. Option F, materialized views, are objects that can store the precomputed results of a query and refresh them periodically.
A Data Engineer is building a set of reporting tables to analyze consumer requests by region for each of the Data Exchange offerings annually, as well as click-through rates for each listing
Which views are needed MINIMALLY as data sources?
The SNOWFLAKE.DATASHARING _USAGE.LISTING_CONSOKE>TION_DAILY view provides information about consumer requests by region for each of the Data Exchange offerings annually, as well as click-through rates for each listing. This view is the minimal data source needed for building the reporting tables. The other views are not relevant for this use case.
Which functions will compute a 'fingerprint' over an entire table, query result, or window to quickly detect changes to table contents or query results? (Select TWO).
The functions that will compute a 'fingerprint' over an entire table, query result, or window to quickly detect changes to table contents or query results are:
HASH_AGG(*): This function computes a hash value over all columns and rows in a table, query result, or window. The function returns a single value for each group defined by a GROUP BY clause, or a single value for the entire input if no GROUP BY clause is specified.
HASH_AGG(<expr>, <expr>): This function computes a hash value over two expressions in a table, query result, or window. The function returns a single value for each group defined by a GROUP BY clause, or a single value for the entire input if no GROUP BY clause is specified. The other functions are not correct because:
HASH (*): This function computes a hash value over all columns in a single row. The function returns one value per row, not one value per table, query result, or window.
HASH_AGG_COMPARE (): This function compares two hash values computed by HASH_AGG() over two tables or query results and returns true if they are equal or false if they are different. The function does not compute a hash value itself, but rather compares two existing hash values.
HASH COMPARE(): This function compares two hash values computed by HASH() over two rows and returns true if they are equal or false if they are different. The function does not compute a hash value itself, but rather compares two existing hash values.
A Data Engineer is investigating a query that is taking a long time to return The Query Profile shows the following:
What step should the Engineer take to increase the query performance?
The step that the Engineer should take to increase the query performance is to increase the size of the virtual warehouse. The Query Profile shows that most of the time was spent on local disk IO, which indicates that the query was reading a lot of data from disk rather than from cache. This could be due to a large amount of data being scanned or a low cache hit ratio. Increasing the size of the virtual warehouse will increase the amount of memory and cache available for the query, which could reduce the disk IO time and improve the query performance. The other options are not likely to increase the query performance significantly. Option A, adding additional virtual warehouses, will not help unless they are used in a multi-cluster warehouse configuration or for concurrent queries. Option C, rewriting the query using Common Table Expressions (CTEs), will not affect the amount of data scanned or cached by the query. Option D, changing the order of the joins and starting with smaller tables first, will not reduce the disk IO time unless it also reduces the amount of data scanned or cached by the query.
