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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. 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?

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

2. You have a Snowflake table named 'ORDERS' with columns 'ORDER D', 'CUSTOMER D', and 'ORDER JSON' (a variant column storing order details). You need to extract specific product names from the 'ORDER JSON' column for each order and return them as a table. The 'ORDER JSON' structure is an array of objects, where each object represents a product with fields like 'product_name' and 'quantity'. Which approach is the most efficient and scalable way to achieve this, considering the possibility of millions of rows in the 'ORDERS table?

A) Create a Java UDF that takes ORDER JSON' as input, parses it using a JSON library, extracts the product names, and returns a comma-separated string. Use a WHILE loop within the UDF to parse the JSON array.
B) Create a SQL UDF that iterates through the JSON array using SQL commands and returns a comma-separated string of product names. Then, use SPLIT TO_TABLE to convert the string to rows.
C) Create a JavaScript UDF that takes ' ORDER_JSON' as input and returns an array of product names. Use 'JSON.parse()' to parse the JSON string and iterate using array methods.
D) Create a Python UDTF that takes 'ORDER JSON' as input, parses it, and yields a row for each product name extracted. Use LATERAL FLATTEN within the UDTF for optimized JSON processing.
E) Use a standard SQL query with LATERAL FLATTEN and JSON VALUE functions to extract product names directly without using a UDF or UDTF.

3. You have a table named 'TRANSACTIONS with the following definition: CREATE TABLE TRANSACTIONS ( TRANSACTION ID NUMBER, TRANSACTION DATE DATE, CUSTOMER_ID NUMBER, AMOUNT PRODUCT_CATEGORY VARCHAR(50) Users frequently query this table using filters on both 'TRANSACTION_DATE and 'PRODUCT CATEGORY. You want to optimize query performance. What is the MOST effective approach?

A) Create a materialized view joining 'TRANSACTIONS' with a dimension table containing product category information.
B) Partition the table by 'TRANSACTION DATE
C) Cluster the table using a composite key of '(TRANSACTION_DATE, PRODUCT CATEGORY)'.
D) Cluster the table on ' TRANSACTION_DATE and then create a materialized view filtered by PRODUCT_CATEGORY&.
E) Create separate indexes on 'TRANSACTION DATE' and 'PRODUCT CATEGORY.

4. A Snowflake data warehouse contains a table 'WEB EVENTS' with columns like 'EVENT ID', 'EVENT TIMESTAMP, 'USER , 'PAGE URL', and 'SESSION ID'. The data engineering team has enabled search optimization on 'PAGE URL' because analysts frequently filter on specific URLs. However, they notice that queries filtering on multiple 'PAGE URL' values (e.g., using 'WHERE PAGE URL IN ('urll', 'ur12', are not performing as well as expected. What are the potential reasons for this behavior, and what strategies can be used to improve performance in this scenario? Select all that apply:

A) Statistics on the 'PAGE URL' column are outdated. Run 'ANALYZE TABLE WEB EVENTS to refresh the statistics.
B) The warehouse size is too small to handle the complexity of the IN list lookup. Increase the warehouse size.
C) Search optimization is not designed to efficiently handle IN list lookups with a large number of values. Consider using a temporary table or common table expression (CTE) to pre-filter the data.
D) The number of distinct values in the 'PAGE URL' column is very high, leading to a large search access path, making IN list lookups inefficient. Consider clustering by PAGE_URL
E) Search optimization is automatically disabled when using IN clause, therefore it is important to rewrite the query without using IN operator.

5. You have created an external table in Snowflake that points to a large dataset stored in Azure Blob Storage. The data consists of JSON files, and you've noticed that query performance is slow. Analyzing the query profile, you see that Snowflake is scanning a large number of unnecessary files. Which of the following strategies could you implement to significantly improve query performance against this external table?

A) Increase the size of the Snowflake virtual warehouse to provide more processing power.
B) Create a materialized view on top of the external table to pre-aggregate the data.
C) Create an internal stage, copy all JSON Files, create and load the target table, and drop external table
D) Convert the JSON files to Parquet format and recreate the external table to point to the Parquet files.
E) Partition the data in Azure Blob Storage based on a relevant column (e.g., date) and define partitioning metadata in the external table definition using PARTITION BY.

Solutions: