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Exam Code: DEA-C02
Exam Name: SnowPro Advanced: Data Engineer (DEA-C02)
No. of Questions: 354 Questions and Answers
Updated: Sep 17, 2026

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Total Questions: 354
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Total Questions: 354
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Snowflake DEA-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Ingestion and Sourcing	20-25%	- Handle different data formats: structured, semi-structured, unstructured - Use Snowflake ingestion methods: Snowpipe, COPY INTO, External Functions - Ingest data from various sources: data lakes, APIs, on-premises systems, cloud storage - Design and implement continuous and batch ingestion pipelines
Topic 2: Data Governance, Security, and Compliance	10-15%	- Apply data protection: encryption, masking, row-level security - Enforce data quality and governance standards - Manage data lineage, cataloging, and compliance policies - Implement access control: RBAC, authentication, authorization
Topic 3: Data Transformation and Processing	20-25%	- Manage data quality, validation, and deduplication - Implement ELT/ETL workflows using Streams, Tasks, and Dynamic Tables - Process semi-structured data: JSON, Avro, Parquet, ORC - Transform and enrich data using SQL, JavaScript, Python, and Snowpark
Topic 4: Performance Optimization and Compute Management	15-20%	- Optimize query performance: clustering, partitioning, materialized views - Use search optimization and query acceleration services - Manage virtual warehouses: sizing, scaling, multi-cluster, cost control - Monitor and tune workloads and resource utilization
Topic 5: Data Pipeline Architecture and Design	15-20%	- Integrate with external tools and platforms: orchestration, BI, ML - Apply design patterns for data engineering workloads - Design scalable, reliable, and maintainable data pipelines - Build end-to-end near real-time streaming solutions
Topic 6: Data Sharing and Collaboration	5-10%	- Work with Snowflake Data Marketplace and external data providers - Implement secure data sharing and data exchanges - Design multi-tenant and cross-account data architectures

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions:

Question #1

You are tasked with creating a development environment from a production database named 'PROD DB'. This database contains sensitive data, and you need to mask the data in the development environment. You decide to use cloning and a transformation function during the cloning process. What is the MOST efficient approach to clone 'PROD DB' into a development database 'DEV DB' and mask sensitive data in the process?

A. Create a clone of 'PROD named 'DEV DB'. Define masking policies on the columns in 'PROD DB' before cloning. These policies will be automatically applied to the cloned tables in "DEV_DB' ensuring all data is masked during query time in the DEV environment.
B. Clone 'PROD to ' DEV DB'. Export the data from 'DEV DB', transform it using a scripting language (e.g., Python), and then load the transformed data back into replacing the original data.
C. Create a clone of 'PROD named 'DEV DB', then create views on 'DEV DB' using masking policies. Cloning the Views from 'PROD will automatically copy the masking policies.
D. Create a clone of 'PROD named 'DEV DB'. Create stored procedures on 'DEV DB' which apply masking at the query level. Cloning databases does not preserve masking policies from the Source database
E. Create a clone of 'PROD named 'DEV DB'. Create a warehouse for running masking policies. Then apply masking policies to the tables in 'DEV DB' Cloning masks the underlying data directly.

Reveal Solution Discussion 0

Correct Answer: A 🗳️

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Question #2

A data engineer is using the Snowflake Spark connector to read a large table from Snowflake into a Spark DataFrame. The table contains a 'TIMESTAMP NTT column. After loading the data, the engineer observes that the values in the 'TIMESTAMP NTZ' column are not preserved accurately when retrieved from the DataFrame. What are the potential issues and what configurations can be adjusted in Snowflake to improve the result?

A. Option E
B. Option C
C. Option D
D. Option B
E. Option A

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Correct Answer: C,D 🗳️

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Question #3

You are developing a Secure UDF in Snowflake to encrypt sensitive customer data'. The UDF should only be accessible by authorized roles. Which of the following steps are essential to properly secure the UDF?

A. Using masking policies instead of Secure UDFs is the recommended approach for data security
B. Setting the 'SECURITY INVOKER clause when creating the UDF to execute the UDF with the privileges of the caller.
C. Ensuring that the UDF is owned by a role with appropriate permissions and limiting access to this role.
D. Granting the EXECUTE privilege on the UDF only to the roles that require access.
E. Using the 'SECURE keyword when creating the UDF to prevent viewing the UDF definition.

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Correct Answer: C,D,E 🗳️

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Question #4

A data engineer is responsible for maintaining a Snowflake data warehouse. They notice a significant slowdown in the performance of a specific query that aggregates data from a table called 'SALES DATA', which contains billions of rows. The query is used for generating daily sales reports. The engineer suspects that the issue might be related to clustering. How would you diagnose the effectiveness of the clustering on the 'SALES DATA' table and identify potential improvements?

A. Use the 'DESCRIBE TABLE SALES_DATA' command and check the 'clustering_key' property, then run 'SELECT SYSTEM$MEASURE CLUSTERING DEPTH('SALES to check the average depth of the table. Compare the clustering depth to the number of micro- partitions to assess clustering effectiveness. A depth closer to zero is best.
B. Use the 'SHOW TABLES command to view the clustering key defined on the table. Verify that the clustering key is appropriate for the query workload.
C. Use the SYSTEM$CLUSTERING_INFORMATION' function to analyze the clustering depth of the table. A high clustering depth indicates poor clustering.
D. Use the 'VALIDATE table command. This command detects fragmentation in the data due to poor clustering.
E. Examine the query profile in the Snowflake web interface to identify stages that are scanning large amounts of data. Check if these stages are benefiting from clustering.

Reveal Solution Discussion 0

Correct Answer: C 🗳️

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Question #5

A. Create a new table with columns for 'item_id' and 'price' using the 'EVENT DATA column. Refreshed in a regular interval and used in Downstream querying.
B. Create a view that casts the 'EVENT DATA' column to VARCHAR before extracting attributes.
C. Use the ' GET_PATH' function repeatedly to extract 'item_id' and 'price' in the main query.
D. Create a virtual column for 'item_id' and 'price' using JSON path expressions and create indexes on these virtual columns.
E. Create a search optimization service for the table 'USER_ACTIVITY to help filtering data in downstream

Reveal Solution Discussion 0

Correct Answer: A,E 🗳️

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