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Snowflake DEA-C02 Exam Syllabus Topics:

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

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

2. Which of the following statements are accurate regarding the differences between SQL UDFs and Java UDFs in Snowflake? (Select two)

A) SQL UDFs can only be used for simple transformations and cannot execute external calls, while Java UDFs can perform complex logic and interact with external services via libraries.
B) SQL UDFs are defined using SQL code within Snowflake, whereas Java UDFs require uploading a JAR file containing the compiled Java code.
C) Java UDFs are deprecated and should not be used; instead, SQL UDFs are recommended for all scenarios.
D) Java UDFs always execute faster than SQL UDFs due to JVM optimizations.
E) SQL UDFs and Java UDFs are interchangeable, and there is no performance difference between them.

3. You are responsible for monitoring the performance of a Snowflake data pipeline that loads data from S3 into a Snowflake table named 'SALES DATA. You notice that the COPY INTO command consistently takes longer than expected. You want to implement telemetry to proactively identify the root cause of the performance degradation. Which of the following methods, used together, provide the MOST comprehensive telemetry data for troubleshooting the COPY INTO performance?

A) Query the 'COPY_HISTORY view and the view in 'ACCOUNT_USAG Also, check the S3 bucket for throttling errors.
B) Use Snowflake's partner connect integrations to monitor the virtual warehouse resource consumption and query the 'VALIDATE function to ensure data quality before loading.
C) Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and enable Snowflake's query profiling for the COPY INTO statement.
D) Query the 'COPY HISTORY view in the 'INFORMATION SCHEMA' and monitor CPU utilization of the virtual warehouse using the Snowflake web I-Jl.
E) Query the ' LOAD_HISTORY function and monitor the network latency between S3 and Snowflake using an external monitoring tool.

4. A data engineer wants to use Snowpark to read a large CSV file from an external stage and infer the schema automatically. However, some columns in the CSV contain data that Snowflake cannot automatically infer the type for. Which of the following code snippets demonstrates the CORRECT way to read the CSV file with schema inference and handle potentially problematic columns by explicitly specifying their data types?

A)

B)

C)

D)

E)

5. 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) The warehouse size is too small to handle the complexity of the IN list lookup. Increase the warehouse size.
B) 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.
C) 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
D) Search optimization is automatically disabled when using IN clause, therefore it is important to rewrite the query without using IN operator.
E) Statistics on the 'PAGE URL' column are outdated. Run 'ANALYZE TABLE WEB EVENTS to refresh the statistics.

Solutions: