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Snowflake DEA-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Performance Optimization	15%	- Data Optimization 1. Materialized views 2. Data cache management 3. Search optimization service - Warehouse Performance 1. Warehouse sizing and selection 2. Resource monitors 3. Warehouse scaling policies 4. Multi-cluster warehouses - Query Optimization 1. Avoiding common performance pitfalls 2. Query profiling and analysis 3. Indexing strategies with clustering 4. Query result caching
Data Transformation with Snowflake	30%	- SQL Transformations 1. Complex JOINs and set operations 2. Window functions advanced usage 3. Data type conversions and handling 4. Working with semi-structured data (VARIANT) - Data Processing Patterns 1. MERGE, UPDATE, DELETE operations 2. Zero-copy cloning for ETL 3. Time travel and change data capture - Snowflake Scripting 1. Procedures and control flow 2. Dynamic SQL 3. Error handling
Data Ingestion and Consumption	20%	- Continuous Data Loading 1. Snowpipe configuration and usage 2. Automating data loading with tasks 3. Real-time data ingestion patterns - Bulk Loading and Unloading 1. Data loading performance optimization 2. Handling staged files 3. File format options (CSV, JSON, Parquet, AVRO) 4. COPY INTO command options and best practices - Data Unloading 1. Partitioning unloading data 2. Unloading to internal and external stages 3. Data export best practices
Security and Governance	15%	- Data Security 1. External tokenization 2. Row-level security policies 3. Data masking and tokenization 4. Column-level security - Governance and Compliance 1. Access history and auditing 2. Object tagging 3. Row access policies 4. Data retention policies - Access Control 1. Role-based access control (RBAC) 2. Role hierarchy and ownership 3. GRANT and REVOKE operations
Data Architecture and Processing	20%	- Data Storage Architecture 1. Micro-partitioning and clustering 2. Table types (Permanent, Transient, Temporary) 3. Hybrid Tables concepts - Data Pipeline Design 1. Data scheduling and orchestration 2. Stream and task patterns 3. Pipeline monitoring and error handling - Data Modeling for Performance 1. Dimension handling 2. Slowly changing dimensions (SCD) 3. Star and snowflake schemas

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

1. You are designing a data pipeline to ingest streaming data from Kafka into Snowflake. The data contains nested JSON structures representing customer orders. You need to transform this data and load it into a flattened Snowflake table named 'ORDERS FLAT'. Given the complexities of real-time data processing and the need for custom logic to handle certain edge cases within the JSON payload, which approach provides the MOST efficient and maintainable solution for transforming and loading this streaming data into Snowflake?

A) Use Snowflake's built-in JSON parsing functions within a Snowpipe COPY INTO statement, combined with a 'CREATE VIEW' statement on top of the loaded data. The view will use 'LATERAL FLATTEN' to present the data in the desired flattened structure without physically transforming the underlying data.
B) Create a Python UDF that calls 'json.loads()' to parse the JSON within Snowflake and then use SQL commands with 'LATERAL FLATTEN' to navigate and extract the desired fields into a staging table. Afterward, use a separate SQL script to insert from staging to the final table 'ORDERS FLAT
C) Implement a custom external function (UDF) written in Java to parse and transform the JSON data before loading it into Snowflake. Configure Snowpipe to call this UDF during the data ingestion process. This UDF will flatten the JSON structure and return a tabular format directly insertable into 'ORDERS FLAT.
D) Utilize a third-party ETL tool (like Apache Spark) to consume the data from Kafka, perform the JSON flattening and transformation logic, and then use the Snowflake connector to load the data into the 'ORDERS FLAT' table in batch mode.
E) Use Snowflake's Snowpipe with a COPY INTO statement that utilizes the 'STRIP OUTER ARRAY option to handle the JSON array, combined with a series of SQL queries with 'LATERAL FLATTEN' functions to extract the nested data after loading into a VARIANT column.

2. You are tasked with creating a Python script to load data from a CSV file stored in an AWS S3 bucket into a Snowflake table. You have the following requirements: 1. Use the 'COPY INTO' command for efficient data loading. 2. Handle potential schema evolution in the CSV file (e.g., new columns being added). 3. Automatically create the target table if it doesn't exist, inferring the schema from the CSV. Which combination of Snowflake Python connector functionalities and 'COPY INTO' options would best address these requirements, assuming you have an AWS IAM role configured for Snowflake access to S3?

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

3. You are troubleshooting a slow-running query that joins a large fact table 'SALES DATA' (100 billion rows) with a smaller dimension table 'CUSTOMER DIM' (1 million rows) on 'CUSTOMER ID. Initial analysis shows that the query is spending significant time in the join operation. You suspect the issue lies with the join strategy being used by Snowflake. Which of the following actions are MOST likely to improve query performance and optimize the join?

A) Ensure that the 'CUSTOMER_ID column in both tables has compatible datatypes and that no implicit type conversions are happening during the join. Also check cardinality of 'CUSTOMER_ID in the SALES DATA table.
B) Increase the virtual warehouse size and monitor for spillover to local disk. If spilling occurs, further increase the warehouse size.
C) Analyze the query profile in Snowflake's web UI and identify if a broadcast join is occurring. If so, consider increasing session parameter (within limits) or re-designing the query to avoid the broadcast join.
D) Ensure both 'SALES DATA' and 'CUSTOMER DIM' are clustered on 'CUSTOMER ID.
E) Convert the query to use a LATERAL FLATTEN function to pre-process the 'CUSTOMER_DIW table before the join.

4. Consider the following Snowflake SQL API call to execute a stored procedure:

A) Set the parameter to and retrieve the result set directly from the API response.
B) Include the stored procedure's fully qualified name (database.schema.procedure_name) in the 'statement' parameter.
C) Use the parameter to specify which external functions are allowed to be called by the procedure.
D) The stored procedure should handle the error handling for network disruptions and automatically retry.
E) Set the 'warehouse' parameter in the SQL API request to ensure the stored procedure uses a specific warehouse size.

5. You are tasked with building a User-Defined Aggregate Function (UDAF) in Snowflake to calculate the weighted average of product prices. The weight is determined by the quantity sold for each product. You have the following table: 'SALES (product_id INTEGER, price quantity INTEGER)'. Which of the following UDAF definitions would correctly calculate the weighted average?