Snowflake DEA-C01 Questions [2026]

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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	Storage and Data Protection: The topic tests the implementation of data recovery features and the understanding of Snowflake's Time Travel and micro-partitions. Engineers are evaluated on their ability to create new environments through cloning and ensure data protection, highlighting essential skills for maintaining Snowflake data integrity and accessibility.
Topic 2	Security: The Security topic of the DEA-C01 test covers the principles of Snowflake security, including the management of system roles and data governance. It measures the ability to secure data and ensure compliance with policies, crucial for maintaining secure data environments for Snowflake Data Engineers and Software Engineers.
Topic 3	Performance Optimization: This topic assesses the ability to optimize and troubleshoot underperforming queries in Snowflake. Candidates must demonstrate knowledge in configuring optimal solutions, utilizing caching, and monitoring data pipelines. It focuses on ensuring engineers can enhance performance based on specific scenarios, crucial for Snowflake Data Engineers and Software Engineers.
Topic 4	Data Transformation: The SnowPro Advanced: Data Engineer exam evaluates skills in using User-Defined Functions (UDFs), external functions, and stored procedures. It assesses the ability to handle semi-structured data and utilize Snowpark for transformations. This section ensures Snowflake engineers can effectively transform data within Snowflake environments, critical for data manipulation tasks.
Topic 5	Data Movement: Snowflake Data Engineers and Software Engineers are assessed on their proficiency to load, ingest, and troubleshoot data in Snowflake. It evaluates skills in building continuous data pipelines, configuring connectors, and designing data sharing solutions.

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Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q260-Q265):

NEW QUESTION # 260
A Data Engineer would like to define a file structure for loading and unloading data Where can the file structure be defined? (Select THREE)

A. MERGE command
B. copy command
C. pipe object
D. FILE FORMAT Object
E. INSERT command
F. stage object

Answer: B,D,F

Explanation:
Explanation
The places where the file format can be defined are copy command, file format object, and stage object. These places allow specifying or referencing a file format that defines how data files are parsed and loaded into or unloaded from Snowflake tables. A file format can include various options, such as field delimiter, field enclosure, compression type, date format, etc. The other options are not places where the file format can be defined. Option B is incorrect because MERGE command is a SQL command that can merge data from one table into another based on a join condition, but it does not involve loading or unloading data files. Option D is incorrect because pipe object is a Snowflake object that can load data from an external stage into a Snowflake table using COPY statements, but it does not define or reference a file format. Option F is incorrect because INSERT command is a SQL command that can insert data into a Snowflake table from literal values or subqueries, but it does not involve loading or unloading data files.

NEW QUESTION # 261
A company needs to build an extract, transform, and load (ETL) pipeline that has separate stages for batch data ingestion, transformation, and storage. The pipeline must store the transformed data in an Amazon S3 bucket. Each stage must automatically retry failures. The pipeline must provide visibility into the success or failure of individual stages.
Which solution will meet these requirements with the LEAST operational overhead?

A. Chain AWS Glue jobs that perform each stage together by using job triggers. Set the MaxRetries field to 0.
B. Schedule Apache Airflow directed acyclic graphs (DAGs) on Amazon Manages Workflows tor Apache Airflow (Amazon MWAA) to orchestrate pipeline steps. Use Amazon Simple Queue Service (Amazon SQS) to ingest data. Use AWS Glue jobs to transform data and store the data in the S3 bucket.
C. Deploy AWS Step Functions workflows to orchestrate AWS Lambda functions that ingest data.
Use AWS Glue jobs to transform the data and store the data in the S3 bucket.
D. Build an Amazon EventBridge based pipeline that invokes AWS Lambda functions to perform each stage.

Answer: C

Explanation:
AWS Step Functions provides managed orchestration with per-step retries, error handling, and execution history. Orchestrating Lambda for ingestion and AWS Glue jobs for transformation to S3 delivers a serverless, low-maintenance pipeline with built-in visibility into each stage's success or failure.

NEW QUESTION # 262
A data engineer is using Amazon Athena to analyze sales data that is in Amazon S3. The data engineer writes a query to retrieve sales amounts for 2023 for several products from a table named sales_data. However, the query does not return results for all of the products that are in the sales_data table. The data engineer needs to troubleshoot the query to resolve the issue.
The data engineer's original query is as follows:
SELECT product_name, sum(sales_amount)
FROM sales_data
WHERE year = 2023
GROUP BY product_name
How should the data engineer modify the Athena query to meet these requirements?

A. Change WHERE year = 2023 to WHERE extract(year FROM sales_data) = 2023.
B. Remove the GROUP BY clause.
C. Replace sum(sales_amount) with count(*) for the aggregation.
D. Add HAVING sum(sales_amount) > 0 after the GROUP BY clause.

Answer: D

Explanation:
https://docs.aws.amazon.com/athena/latest/ug/select.html

NEW QUESTION # 263
To view/monitor the clustering metadata for a table, Snowflake provides which of the following system functions?

A. SYSTEM$CLUSTERING_DEPTH_KEY
B. SYSTEM$CLUSTERING_INFORMATION (including clustering depth)
C. SYSTEM$CLUSTERING_KEY_INFORMATION (including clustering depth)
D. SYSTEM$CLUSTERING_DEPTH

Answer: B,D

Explanation:
Explanation
SYSTEM$CLUSTERING_DEPTH:
Computes the average depth of the table according to the specified columns (or the clustering key defined for the table). The average depth of a populated table (i.e. a table containing data) is always 1 or more. The smaller the average depth, the better clustered the table is with regards to the speci-fied columns.
Calculate the clustering depth for a table using two columns in the table:
SELECT SYSTEM$CLUSTERING_DEPTH('TPCH_PRODUCT', '(C2, C9)');
SYSTEM$CLUSTERING_INFORMATION:
Returns clustering information, including average clustering depth, for a table based on one or more columns in the table.
SELECT SYSTEM$CLUSTERING_INFORMATION('SAMPLE_TABLE', '(col1, col3)');

NEW QUESTION # 264
A company wants to migrate data from an Amazon RDS for PostgreSQL DB instance in the eu- east-1 Region of an AWS account named Account_A. The company will migrate the data to an Amazon Redshift cluster in the eu-west-1 Region of an AWS account named Account_B.
Which solution will give AWS Database Migration Service (AWS DMS) the ability to replicate data between two data stores?

A. Set up an AWS DMS replication instance in Account_B in eu-east-1.
B. Set up an AWS DMS replication instance in Account_A in eu-east-1.
C. Set up an AWS DMS replication instance in Account_B in eu-west-1.
D. Set up an AWS DMS replication instance in a new AWS account in eu-west-1.

Answer: A

Explanation:
https://docs.aws.amazon.com/dms/latest/userguide/CHAP_Target.Redshift.html#CHAP_Target.R edshift.Prerequisites

NEW QUESTION # 265
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