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

DEA-C02 real exams

Exam Code: DEA-C02

Exam Name: SnowPro Advanced: Data Engineer (DEA-C02)

Updated: Aug 06, 2026

Q & A: 354 Questions and Answers

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Snowflake DEA-C02 Exam Syllabus Topics:

SectionWeightObjectives
Data Ingestion and Consumption20%- Bulk Loading and Unloading
  • 1. File format options (CSV, JSON, Parquet, AVRO)
  • 2. Handling staged files
  • 3. Data loading performance optimization
  • 4. COPY INTO command options and best practices
- Data Unloading
  • 1. Data export best practices
  • 2. Unloading to internal and external stages
  • 3. Partitioning unloading data
- Continuous Data Loading
  • 1. Automating data loading with tasks
  • 2. Snowpipe configuration and usage
  • 3. Real-time data ingestion patterns
Data Transformation with Snowflake30%- SQL Transformations
  • 1. Data type conversions and handling
  • 2. Window functions advanced usage
  • 3. Working with semi-structured data (VARIANT)
  • 4. Complex JOINs and set operations
- Data Processing Patterns
  • 1. Time travel and change data capture
  • 2. MERGE, UPDATE, DELETE operations
  • 3. Zero-copy cloning for ETL
- Snowflake Scripting
  • 1. Error handling
  • 2. Dynamic SQL
  • 3. Procedures and control flow
Security and Governance15%- Access Control
  • 1. GRANT and REVOKE operations
  • 2. Role hierarchy and ownership
  • 3. Role-based access control (RBAC)
- Data Security
  • 1. Column-level security
  • 2. Row-level security policies
  • 3. External tokenization
  • 4. Data masking and tokenization
- Governance and Compliance
  • 1. Access history and auditing
  • 2. Data retention policies
  • 3. Object tagging
  • 4. Row access policies
Data Architecture and Processing20%- Data Pipeline Design
  • 1. Pipeline monitoring and error handling
  • 2. Data scheduling and orchestration
  • 3. Stream and task patterns
- Data Storage Architecture
  • 1. Hybrid Tables concepts
  • 2. Table types (Permanent, Transient, Temporary)
  • 3. Micro-partitioning and clustering
- Data Modeling for Performance
  • 1. Star and snowflake schemas
  • 2. Dimension handling
  • 3. Slowly changing dimensions (SCD)
Performance Optimization15%- Data Optimization
  • 1. Data cache management
  • 2. Search optimization service
  • 3. Materialized views
- Query Optimization
  • 1. Indexing strategies with clustering
  • 2. Query profiling and analysis
  • 3. Query result caching
  • 4. Avoiding common performance pitfalls
- Warehouse Performance
  • 1. Warehouse scaling policies
  • 2. Resource monitors
  • 3. Warehouse sizing and selection
  • 4. Multi-cluster warehouses

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

1. You're designing a data masking solution for a 'CUSTOMER' table with columns like 'CUSTOMER ID', 'NAME', 'EMAIL', and 'PHONE NUMBER. You want to implement the following requirements: 1. The 'SUPPORT' role should be able to see the last four digits of the 'PHONE NUMBER and a hashed version of the 'EMAIL'. 2. The 'MARKETING' role should be able to see the full 'NAME' and a domain-only version of the 'EMAIL' (everything after the '@' symbol). 3. All other roles should see masked values for 'EMAIL' and 'PHONE NUMBER. Which of the following masking policy definitions BEST achieves these requirements using Snowflake's built-in functions and RBAC?

A)

B)

C)

D)

E)


2. A data engineering team is building a real-time fraud detection system. They have a large 'TRANSACTIONS table that grows rapidly. They need to calculate the average transaction amount per merchant daily. The following query is used:

This query is run every hour and is performance-critical. Which of the following materialized view definitions would provide the BEST performance improvement, considering the need for near real-time data and minimal latency?

A) Option A
B) Option E
C) Option D
D) Option C
E) Option B


3. A data warehousing team is experiencing inconsistent query performance on a large fact table C SALES FACT) that is updated daily. Some queries involving complex joins and aggregations take significantly longer to execute than others, even when run with the same virtual warehouse size. You suspect that the query result cache is not being effectively utilized due to variations in query syntax and the dynamic nature of the data'. Which of the following strategies could you implement to maximize the effectiveness of the query result cache and improve query performance consistency? Assume virtual warehouse size is large and the data is skewed across days.

A) Use stored procedures with parameters to encapsulate the queries. This will ensure that the query syntax is consistent, regardless of the specific parameters used.
B) Implement a data masking policy on the 'SALES_FACT table. Data masking will reduce the size of the data that needs to be cached, improving cache utilization.
C) Implement query tagging to standardize query syntax. By applying consistent tags to queries, you can ensure that similar queries are recognized as identical and reuse cached results.
D) Create a separate virtual warehouse specifically for running these queries. This will isolate the cache and prevent it from being invalidated by other queries.
E) Optimize the 'SALES_FACT table by clustering it on the most frequently used filter columns and enabling automatic clustering. This will improve data locality and reduce the amount of data that needs to be scanned.


4. You are monitoring a Snowpipe pipeline that loads data from an external stage into a Snowflake table. You observe the following error messages in the PIPE ERRORS view: 'Invalid UTF-8 detected in string'. The data files on the stage are encoded in UTF-8. Which of the following actions, taken individually or in combination, are MOST likely to resolve this issue? (Select TWO)

A) Modify the COPY INTO statement to include the 'ON ERROR = 'SKIP_FILE" option.
B) Drop and recreate the external stage with 'TYPE = INTERNAL'.
C) Verify the data files on the stage are actually valid UTF-8 and contain no corrupted characters.
D) Ensure the file format definition explicitly specifies 'ENCODING = 'UTF8".
E) Convert the problematic files to UTF-16 encoding before loading them into the stage.


5. You have implemented a masking policy on the 'SSN' column of the 'EMPLOYEES' table. You now need to suspend the masking policy temporarily for a specific batch job that requires access to the unmasked data'. What is the recommended way to achieve this without dropping the masking policy or altering the user's role?

A) Set the 'DISABLE MASKING' session parameter to 'TRUE' for the batch job session. This will temporarily disable all masking policies.
B) Create a new role with 'ACCOUNTADMIN' privileges, assign this role to the batch job process during the execution, and then revert back to the original role after the job is done.
C) Create a temporary view on the 'EMPLOYEES' table without the 'SSN' column, grant access to the view for the batch job, and drop the view after the job is complete.
D) Grant the 'APPLY MASKING POLICY privilege to the user running the batch job, allowing them to bypass the masking policy.
E)


Solutions:

Question # 1
Answer: C
Question # 2
Answer: A
Question # 3
Answer: A,E
Question # 4
Answer: C,D
Question # 5
Answer: E

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