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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. Consider the following Snowflake UDTF definition written in Python:

Which of the following statements are TRUE regarding the deployment and usage of this UDTF?

A) The return type of the generator 'yield' must strictly adhere to the declared output schema , or errors will occur during execution.
B) The UDTF needs to be registered using 'session.udtf.register' or 'create or replace function' with the 'imports' clause referencing the Python file, and the handler' specifying the function name.
C) The UDTF will automatically be available in all schemas across all databases in the Snowflake account.
D) The UDTF can be called directly in SQL using 'SELECT FROM TABLE(process_json(VARlANT COLUMN));' without any prior registration.
E) The library needs to be explicitly installed and configured within the UDTF's environment using a Snowpark session.

2. A data engineering team is loading a large fact table 'SALES DATA' daily, partitioned by 'SALE DATE. After several months, query performance degrades significantly. An analyst reports that queries filtering on 'CUSTOMER are slow, despite 'CUSTOMER ID' having high cardinality. The table definition is as follows: CREATE TABLE SALES_DATA ( SALE DATE DATE NOT NULL, CUSTOMER_ID NUMBER NOT NULL, PRODUCT ID NUMBER NOT NULL, SALE_AMOUNT ... Which of the following actions would BEST improve query performance for queries filtering on 'CUSTOMER ID', considering the existing partitioning by 'SALE DATE'?

A) Cluster the 'SALES DATA' table on 'CUSTOMER ID.
B) Partition the table by 'CUSTOMER_ID instead of 'SALE_DATE.
C) Create a secondary index on 'CUSTOMER ID'
D) Create a materialized view that aggregates data by 'CUSTOMER_ID and relevant dimensions.
E) Increase the virtual warehouse size.

3. A data engineer is working with a Snowpark DataFrame 'sales df containing sales data with columns 'product id', 'sale_date', and 'sale amount'. The engineer needs to calculate the cumulative sales amount for each product over time. Which of the following code snippets using window functions correctly calculates the cumulative sales amount, ordered by 'sale date'?

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

4. A data engineer is tasked with creating an external table that points to a directory in AWS S3 containing CSV files. The files have a header row and are comma-delimited. The engineer executes the following DDL statement:

A) The statement will succeed if a file format named 'csv_format' with the specified properties already exists.
B) The statement will fail because the 'LOCATION' is missing the protocol (e.g., 's3:/P).
C) The statement will fail because external tables cannot be created directly from CSV files without defining a stage.
D) The statement will fail because the 'SKIP HEADER property is invalid and the 'skip_header' parameter should be used in the 'CREATE FILE FORMAT statement.
E) The statement will fail because the FIELD_DELIMITER property should be 'DELIMITER.

5. 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 C
B) Option A
C) Option D
D) Option B
E) Option E

Solutions: