Snowflake DEA-C01 Questions - Latest Preparation Material [2026]

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Snowflake DEA-C01 Exam Overview:

Certification Vendor:Snowflake
Exam Name:SnowPro Advanced: Data Engineer Certification Exam
Exam Number:DEA-C01
Real Exam Qty:approximately 50 questions
Exam Duration:90 minutes
Available Languages:English
Related Certifications:SnowPro Core Certification (Recommended Prerequisite)
Exam Price:$375 USD
Certificate Validity Period:2 years (recertification required)
Exam Format:Multiple Choice, Multiple Select
Passing Score:~70% (exact passing score not publicly disclosed by Snowflake)
Sample Questions:Snowflake DEA-C01 Sample Questions
Exam Way:Online proctored exam (via Pearson VUE) or in-person testing center
Pre Condition:SnowPro Core Certification is strongly recommended before attempting this advanced-level exam. Practical experience with Snowflake data engineering projects is essential.
Official Syllabus URL:https://www.snowflake.com/certification/

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Snowflake DEA-C01 Exam Syllabus Topics:

TopicDetails
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
  • 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
  • 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 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.

Snowflake SnowPro Advanced: Data Engineer Certification Exam Sample Questions (Q311-Q316):

NEW QUESTION # 311
A company is planning to migrate on-premises Apache Hadoop clusters to Amazon EMR. The company also needs to migrate a data catalog into a persistent storage solution.
The company currently stores the data catalog in an on-premises Apache Hive metastore on the Hadoop clusters. The company requires a serverless solution to migrate the data catalog.
Which solution will meet these requirements MOST cost-effectively?

Answer: A

Explanation:
https://aws.amazon.com/blogs/big-data/migrate-and-deploy-your-apache-hive-metastore-on- amazon-emr/ Migrating the Hive metastore into Amazon EMR and using AWS Glue Data Catalog as an external catalog provides a balance between leveraging the scalable and managed services of AWS (like EMR and Glue Data Catalog) and ensuring a smooth transition from the on-premises setup. This approach leverages the serverless nature of AWS Glue Data Catalog, minimizing operational overhead and potentially reducing costs compared to managing database servers.


NEW QUESTION # 312
A company plans to provision a log delivery stream within a VPC. The company configured the VPC flow logs to publish to Amazon CloudWatch Logs. The company needs to send the flow logs to Splunk in near real time for further analysis.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: A

Explanation:
Kinesis Data Firehose has built-in support for Splunk as a destination, making the integration straightforward. Using a CloudWatch Logs subscription filter directly to Firehose simplifies the data flow, eliminating the need for additional Lambda functions or custom integrations.


NEW QUESTION # 313
A financial company recently added more features to its mobile app. The new features required the company to create a new topic in an existing Amazon Managed Streaming for Apache Kafka (Amazon MSK) cluster.
A few days after the company added the new topic, Amazon CloudWatch raised an alarm on the RootDiskUsed metric for the MSK cluster.
How should the company address the CloudWatch alarm?

Answer: B

Explanation:
The RootDiskUsed metric in Amazon MSK indicates that the storage space on the broker's disk is filling up. To resolve this, the company can expand the storage for the MSK brokers to prevent them from running out of disk space.
Amazon MSK supports automatic storage scaling, which can be configured to avoid similar issues in the future. By enabling auto-scaling, MSK will automatically expand the storage when needed, ensuring that the brokers have enough space to handle new topics or additional data.
ZooKeeper is used for managing the state of Kafka brokers, but the RootDiskUsed metric pertains to the Kafka brokers, not the ZooKeeper nodes.
Expanding ZooKeeper storage will not resolve the issue.
While increasing the instance size may improve performance, it does not address the issue of running out of disk space on the broker's root volume, which is causing the CloudWatch alarm.
Kafka topics do not have a direct volume specification. Storage for Kafka is managed at the broker level, not at the individual topic level. Therefore, this option does not apply to the issue of disk usage.


NEW QUESTION # 314
A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records.
A data engineer needs to find a solution to process the streaming data. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data.
Which solution will meet these requirements with the LEAST operational overhead?

Answer: B

Explanation:
https://docs.aws.amazon.com/redshift/latest/dg/materialized-view-streaming-ingestion.html Use the Streaming Ingestion Feature of Amazon Redshift: Amazon Redshift recently introduced streaming data ingestion, allowing Redshift to consume data directly from Kinesis Data Streams in near real-time. This feature simplifies the architecture by eliminating the need for intermediate steps or services, and it is specifically designed to support near real-time analytics. The operational overhead is minimal since the feature is integrated within Redshift.


NEW QUESTION # 315
A company uses Amazon S3 to store semi-structured data in a transactional data lake. Some of the data files are small, but other data files are tens of terabytes.
A data engineer must perform a change data capture (CDC) operation to identify changed data from the data source. The data source sends a full snapshot as a JSON file every day and ingests the changed data into the data lake.
Which solution will capture the changed data MOST cost-effectively?

Answer: A


NEW QUESTION # 316
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