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Snowflake DEA-C02 Exam Syllabus Topics:

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

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

1. You have a Snowpark DataFrame 'df_products' with columns 'product id', 'category', and 'price'. You need to perform the following transformations in a single, optimized query using Snowpark Python: 1. Filter for products in the 'Electronics' or 'Clothing' categories. 2. Group the filtered data by category. 3. Calculate the average price for each category. 4. Rename the aggregated column to 'average_price'. Which of the following code snippets demonstrates the most efficient way to achieve this?

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

2. A Snowflake table, contains product information in a VARIANT column named This column holds JSON structures. You need to create a view, , that exposes specific fields , and 'category') as structured columns, and should gracefully handle scenarios where may contain characters incompatible with VARCHAR, 'category' is nested inside an array called 'tags'. What is the BEST and the MOST robust approach?

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

3. You are using the Snowflake Spark connector to update records in a Snowflake table based on data from a Spark DataFrame. The Snowflake table 'CUSTOMER' has columns 'CUSTOMER ID' (primary key), 'NAME, and 'ADDRESS'. You have a Spark DataFrame with updated 'NAME and 'ADDRESS' values for some customers. To optimize performance and minimize data transfer, which of the following strategies can you combine with a temporary staging table to perform an efficient update?

A) Use Spark's foreachPartition to batch update statements and execute on each partition. This will help with efficient data transfer and avoid single row based updates.
B) Write the Spark DataFrame to a temporary table in Snowflake. Then, execute an 'UPDATE statement in Snowflake joining the temporary table with the 'CUSTOMER table using the 'CUSTOMER_ID to update the 'NAME and 'ADDRESS' columns. Finally, drop the temporary table.
C) Write the Spark DataFrame to a temporary table in Snowflake using MERGE. Use the WHEN MATCHED clause for Update the target table based on updates from staging table and finally drop the staging table
D) Iterate through each row in the Spark DataFrame and execute an individual 'UPDATE statement against the 'CUSTOMER table in Snowflake. Use the 'CUSTOMER_ID in the 'WHERE clause.
E) Broadcast the Spark DataFrame to all executor nodes, then use a UDF to execute the 'UPDATE' statement for each row directly from Spark.

4. You are setting up a Kafka connector to load data from a Kafka topic into a Snowflake table. You want to use Snowflake's automatic schema evolution feature to handle potential schema changes in the Kafka topic. Which of the following is the correct approach to enable and configure automatic schema evolution using the Kafka Connector for Snowflake?

A) Automatic schema evolution is not directly supported by the Kafka Connector for Snowflake. You must manually manage schema changes in Snowflake.
B) Set the property to 'true' and the 'snowflake.ingest.stage' to an existing stage.
C) Set the 'snowflake.data.field.name' property to the name of the column in the Snowflake table where the JSON data will be stored as a VARIANT, and set 'snowflake.enable.schematization' to 'true'.
D) Set 'snowflake.ingest.file.name' to an existing file in a stage.
E) Set the 'value.converter.schemas.enable' to 'true' and provide Avro schemas and also, configure the Snowflake table with appropriate data types for each field. Schema Evolution is not supported by the Kafka Connector for Snowflake.

5. You have a Snowflake Stream named 'ORDERS STREAM' on an 'ORDERS' table, which is used to incrementally load data into a historical orders table named 'HISTORICAL ORDERS'. The data pipeline involves a series of tasks: 1) Consume changes from the 'ORDERS STREAM', 2) Apply transformations and data quality checks, and 3) Merge the changes into 'HISTORICAL ORDERS' using a MERGE statement. After a recent data load, you notice that the 'HISTORICAL ORDERS' table contains duplicate records for certain 'ORDER values. The MERGE statement uses 'ORDER ID' as the matching key. You have confirmed that the transformation logic is correct and idempotent. Examine the MERGE statement below. What could be causing the duplicates, given the context of Streams and incremental loading?

A) Multiple tasks are concurrently consuming from the same 'ORDERS STREAM' without proper coordination, causing records to be processed multiple times.
B) The stream's or 'BEFORE clause is being used incorrectly, potentially rewinding the stream to an earlier point in time.
C) The MERGE statement is not correctly handling updates and deletes from the stream. The 'WHEN NOT MATCHED' and 'WHEN MATCHED' clauses are not mutually exclusive, leading to potential insertions of duplicate rows.
D) The 'ORDERS STREAM' is retaining historical data beyond the data retention period, causing older records to be re-processed.
E) The stream is not configured to capture DELETE operations from the ORDERS table, causing records that should have been removed in HISTORICAL ORDERS to remain.

Solutions:

Question # 1
Answer: E

Question # 2
Answer: A

Question # 3
Answer: B,C

Question # 4
Answer: A

Question # 5
Answer: A