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Snowflake DEA-C02 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: 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 2: Data Ingestion and Sourcing | 20-25% | - Design and implement continuous and batch ingestion pipelines - Use Snowflake ingestion methods: Snowpipe, COPY INTO, External Functions - Ingest data from various sources: data lakes, APIs, on-premises systems, cloud storage - Handle different data formats: structured, semi-structured, unstructured |
| Topic 3: Data Pipeline Architecture and Design | 15-20% | - Integrate with external tools and platforms: orchestration, BI, ML - Design scalable, reliable, and maintainable data pipelines - Apply design patterns for data engineering workloads - Build end-to-end near real-time streaming solutions |
| Topic 4: Data Transformation and Processing | 20-25% | - Implement ELT/ETL workflows using Streams, Tasks, and Dynamic Tables - Manage data quality, validation, and deduplication - Process semi-structured data: JSON, Avro, Parquet, ORC - Transform and enrich data using SQL, JavaScript, Python, and Snowpark |
| Topic 5: Performance Optimization and Compute Management | 15-20% | - Monitor and tune workloads and resource utilization - Use search optimization and query acceleration services - Optimize query performance: clustering, partitioning, materialized views - Manage virtual warehouses: sizing, scaling, multi-cluster, cost control |
| 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:
1. You are working with a Snowflake table 'customer_data' which contains customer information stored in a VARIANT column named raw_info'. The 'raw_info' JSON structure includes nested addresses, and preferences. Your task is to extract the city from the first address in the 'addresses' array, and the customer's preferred communication method from the 'preferences' object. Some customers might not have addresses or preferences defined. Select the two SQL snippets that correctly and efficiently extract this data, handling missing fields gracefully and providing appropriate type casting. Address array is in the format 'addresses: [ { 'city': '...', 'state': ' '},
A) Option D
B) Option A
C) Option C
D) Option B
E) Option E
2. You have a requirement to create a UDF in Snowflake that transforms data based on a complex set of rules defined in an external Python library. The library requires specific dependencies. You also need to ensure the UDF is secure and that the code is not visible to unauthorized users. Which of the following steps MUST be taken to achieve this?
A) Create a Snowflake Anaconda environment specifying the required Python library dependencies. Then, create a Python UDF, reference the Anaconda environment, and use the 'SECURE' keyword.
B) Package all the Python libaries code into one file, then create an Javascript UDF and load/execute the python code inside the Javascript UDF.
C) Create an external function pointing to an AWS Lambda function or Azure Function that hosts the Python code and its dependencies. Secure the external function using API integration and role-based access control.
D) Upload the Python library and its dependencies as internal stages. Create a Java UDF that executes the Python code using the 'ProcessBuilder' class. Mark the Java UDF as 'SECURE'
E) Create a Python UDF and directly upload the Python library code into the UDF's body. Snowflake automatically manages dependencies for UDFs.
3. Which of the following statements are true regarding data masking policies in Snowflake? (Select all that apply)
A) The 'CURRENT_ROLE()' function can be used within a masking policy to implement role-based data masking.
B) Once a masking policy is applied to a column, the original data is permanently altered.
C) Data masking policies are supported on external tables.
D) Data masking policies can be applied to both tables and views.
E) Different masking policies cannot be applied to different columns within the same table.
4. A financial services company is using Snowflake Streams on a table 'TRANSACTIONS' to capture changes for auditing purposes. The 'TRANSACTIONS' table contains sensitive data, and the auditing team requires the stream to only capture changes to specific columns: 'ACCOUNT ID', 'TRANSACTION DATE', and 'TRANSACTION AMOUNT'. Which of the following approaches is the MOST efficient and secure way to achieve this requirement, ensuring minimal performance impact and data exposure?
A) Create a Stream on the 'TRANSACTIONS' table and use a masking policy on the stream's output to redact the unnecessary columns.
B) Create a View that selects only the 'ACCOUNT ID, 'TRANSACTION DATE, and 'TRANSACTION AMOUNT columns and create a Stream on the View.
C) Create a task that clones the TRANSACTIONS table and a stream on that cloned table, limiting what changes are captured using a WHERE clause on the cloning command.
D) Create a standard Stream on the 'TRANSACTIONS table and then filter the results in downstream processing to only include the required columns.
E) Create a Stream on the 'TRANSACTIONS' table. Periodically truncate stream and reload all data from TRANSACTION table by applying filter while loading.
5. 
A) Create a virtual column for 'item_id' and 'price' using JSON path expressions and create indexes on these virtual columns.
B) Use the ' GET_PATH' function repeatedly to extract 'item_id' and 'price' in the main query.
C) Create a search optimization service for the table 'USER_ACTIVITY to help filtering data in downstream
D) Create a view that casts the 'EVENT DATA' column to VARCHAR before extracting attributes.
E) 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.
Solutions:
| Question # 1 Answer: A,E | Question # 2 Answer: A | Question # 3 Answer: A,C,D | Question # 4 Answer: B | Question # 5 Answer: C,E |
