Online DEA-C01 Version: Unparalleled SnowPro Advanced: Data Engineer Certification Exam - Free PDF Quiz 2026 DEA-C01

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Snowflake DEA-C01 Exam Overview:

Certification Vendor:	Snowflake
Exam Name:	SnowPro Advanced: Data Engineer Certification Exam
Exam Number:	DEA-C01
Certificate Validity Period:	2 years
Real Exam Qty:	Approximately 65 questions
Exam Format:	Multiple select, Scenario-based questions, Multiple choice
Passing Score:	Not publicly disclosed (Snowflake uses scaled scoring)
Related Certifications:	SnowPro Core Certification
Exam Price:	$375 USD
Exam Duration:	115 minutes
Available Languages:	English
Recommended Training:	Snowflake University Training SnowPro Advanced Data Engineer Exam Guide
Exam Registration:	Snowflake Certification Portal
Sample Questions:	Snowflake DEA-C01 Sample Questions
Exam Way:	Online proctored or authorized testing center
Pre Condition:	Recommended: SnowPro Core Certification or equivalent Snowflake experience
Official Syllabus URL:	https://www.snowflake.com/certifications/snowpro-advanced-data-engineer/

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Snowflake DEA-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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.
Topic 2	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 3	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 4	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 5	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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q165-Q170):

NEW QUESTION # 165
A data engineer runs Amazon Athena queries on data that is in an Amazon S3 bucket. The Athena queries use AWS Glue Data Catalog as a metadata table.
The data engineer notices that the Athena query plans are experiencing a performance bottleneck. The data engineer determines that the cause of the performance bottleneck is the large number of partitions that are in the S3 bucket. The data engineer must resolve the performance bottleneck and reduce Athena query planning time.
Which solutions will meet these requirements? (Choose two.)

A. Bucket the data based on a column that the data have in common in a WHERE clause of the user query.
B. Use the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects.
C. Transform the data that is in the S3 bucket to Apache Parquet format.
D. Create an AWS Glue partition index. Enable partition filtering.
E. Use Athena partition projection based on the S3 bucket prefix.

Answer: D,E

Explanation:
https://aws.amazon.com/blogs/big-data/top-10-performance-tuning-tips-for-amazon-athena/ Optimizing Partition Processing using partition projection Processing partition information can be a bottleneck for Athena queries when you have a very large number of partitions and aren't using AWS Glue partition indexing. You can use partition projection in Athena to speed up query processing of highly partitioned tables and automate partition management. Partition projection helps minimize this overhead by allowing you to query partitions by calculating partition information rather than retrieving it from a metastore. It eliminates the need to add partitions' metadata to the AWS Glue table.

NEW QUESTION # 166
Which privilege are required on an object (i.e. user or role) with USERADMIN Role can modify the object properties?

A. OWNERSHIP
B. MODIFY
C. OPEARTE
D. MANAGE GRANTS

Answer: A

NEW QUESTION # 167
Snowpipe loads data from files as soon as they are available in a stage. Automated data loads lever-age event notifications for cloud storage to inform Snowpipe of the arrival of new data files to load. Which Cloud hosted platform provides cross cloud support for automated data loading via Snow-pipe?

A. AWS
B. AZURE
C. None of the Above currently provide cross cloud support for Snowpipe.
D. GCP

Answer: A

Explanation:
Explanation
Cross-cloud support only available to accounts hosted on Amazon Web Services currently.

NEW QUESTION # 168
Data Engineer wants to analyze query performance & looking out for profiling information, He went to Query/Operator Details also called Profile Overview of Query Profile Interface & searching for statistics attributes around I/O. Which of the following information he can't get from there?

A. Percentage scanned from cache - the percentage of data scanned from the local disk cache.
B. Bytes sent over the wireframe - amount of data sent over the wireframe
C. Bytes written - bytes written (e.g. when loading into a table).
D. Bytes read from result - bytes read from the result object.
E. External bytes scanned - bytes read from an external object, e.g. a stage.

Answer: B

Explanation:
Explanation
To help you analyze query performance, Query/Operator Details panel also called Profile overview panel provides two classes of profiling information:
Execution time, broken down into categories
Detailed statistics
Apart from Option a Bytes sent over the wireframe - amount of data sent over the wireframe , Rest of the Statistics Information provided by Query/Operator details in the Query Profile Inter-face.
To Know More about the Query/Operator Details options , please refer the link:
https://docs.snowflake.com/en/user-guide/ui-query-profile#query-operator-details

NEW QUESTION # 169
A company uses an Amazon Redshift cluster to manage data, including vendor sales data. The company wants to store a copy of the vendor data in an Amazon S3 bucket.
A data engineer sets up an AWS Glue job to upload the data to the S3 bucket data on a schedule. The data engineer set up a network connection to allow private traffic between Amazon Redshift and Amazon S3.
What is the next step required to meet this requirement?

A. Enable the Amazon Redshift data sharing feature. Set the S3 bucket as a target bucket for data sharing.
B. Store login credentials for Amazon Redshift in AWS Secrets Manager. Add a reference to the secret to the Glue job configuration.
C. Add the S3 bucket to an AWS Glue Data Catalog. Configure Amazon Redshift Spectrum to access the Data Catalog.
D. Create an IAM role that has permission to write to the S3 bucket. Associate the IAM role with the Amazon Redshift cluster.

Answer: D

Explanation:
Amazon Redshift needs an IAM role that grants permission to write to the target Amazon S3 bucket. After the private network path is in place, associating that role with the Redshift cluster enables the export operation to store the vendor data in S3.

NEW QUESTION # 170
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