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

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Snowflake DEA-C02 Exam Syllabus Topics:

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

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E) Statistics on the 'PAGE URL' column are outdated.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

2. A data engineering team has implemented a continuous data pipeline that loads data into a Snowflake table named 'SALES DATA' They notice that the pipeline intermittently experiences performance degradation, particularly during peak business hours. The team wants to implement alerts to proactively identify and address these performance issues. Which of the following approaches would be MOST effective for monitoring the pipeline and triggering alerts based on specific performance metrics related to data loading?

A) Implement a data streaming service that monitors the 'SALES_DATX table in real-time. The streaming service should track the number of rows inserted per minute and trigger an alert if the insertion rate drops below a predefined threshold. No Snowflake object or Alert required.
B) create a custom Snowflake Alert that triggers when the function for the 'SALES_DATA' table indicates significant delay in data loading. Use a Snowflake Notification Integration to send alerts via email or Slack.
C) Enable Snowflake's query acceleration service. This service automatically analyzes query performance and identifies opportunities for optimization, removing the need for manual monitoring and alerting. Use Snowflake's resource monitors to track credit usage.
D) Create a Snowflake Task that periodically queries the 'QUERY_HISTORY view, calculates the average load duration for 'SALES_DATX, and triggers an alert if the duration exceeds a predefined threshold. Use a Stored Procedure to handle the alert logic and send notifications.
E) Create a Snowflake Alert based on a metric in the Account Usage views (e.g., that identifies when load durations for the data warehouse associated with the data pipeline exceed a specified threshold. Configure a Notification Integration to route alerts to a designated channel.

3. You are building a data pipeline to ingest clickstream data into Snowflake. The raw data is landed in a stage and you are using a Stream on this stage to track new files. The data is then transformed and loaded into a target table 'CLICKSTREAM DATA. However, you notice that sometimes the same files are being processed multiple times, leading to duplicate records in 'CLICKSTREAM DATA. You are using the 'SYSTEM$STREAM HAS DATA' function to check if the stream has data before processing. What are the possible reasons this might be happening, and how can you prevent it? (Select all that apply)

A) The auto-ingest notification integration is configured incorrectly, causing duplicate notifications to be sent for the same files. This is particularly applicable when using cloud storage event triggers.
B) The transformation process is not idempotent. Even with the same input files, it produces different outputs each time it runs.
C) The 'SYSTEM$STREAM HAS DATA' function is unreliable and should not be used for production data pipelines. Use 'COUNT( on the stream instead.
D) The stream offset is not being advanced correctly after processing the files. Ensure that the files are consumed completely and a DML operation is performed to acknowledge consumption.
E) The COPY INTO command used to load the files into Snowflake has the 'ON ERROR = CONTINUE option set, allowing it to skip corrupted files, causing subsequent processing to pick them up again.

4. You're designing a data masking solution for a 'CUSTOMER' table with columns like 'CUSTOMER ID', 'NAME', 'EMAIL', and 'PHONE NUMBER. You want to implement the following requirements: 1. The 'SUPPORT' role should be able to see the last four digits of the 'PHONE NUMBER and a hashed version of the 'EMAIL'. 2. The 'MARKETING' role should be able to see the full 'NAME' and a domain-only version of the 'EMAIL' (everything after the '@' symbol). 3. All other roles should see masked values for 'EMAIL' and 'PHONE NUMBER. Which of the following masking policy definitions BEST achieves these requirements using Snowflake's built-in functions and RBAC?

A)

B)

C)

D)

E)

5. You are designing a CI/CD pipeline for your Snowflake data transformations. One stage involves testing a new stored procedure that modifies several tables in your data warehouse. To ensure data integrity and proper rollback capabilities during testing in your development environment, you want to use a combination of cloning and Tme Travel. Select the option that represents the most robust strategy for testing with the ability to revert to the original state in case of failures. Choose all that apply.

A) Create a new database by cloning the existing database before the transformations. Use the cloned database for all development and testing.
B) Immediately after executing the stored procedure, execute 'UNDROP TABLE' on all the affected tables. If there were errors, it would restore data from the point of the procedure execution.
C) Before executing the stored procedure in development, create clones of all affected tables and store the clone names in a configuration file for rollback if needed.
D) Create a clone of the schema containing all the tables affected by the stored procedure before executing the procedure in the development environment. This will create point in time backup to rollback at schema level.
E) Leverage Snowflake's Time Travel feature to create a named snapshot of each affected table immediately before running the stored procedure. Use the 'AT(TIMESTAMP ...)' or 'BEFORE(STATEMENT => ...)' syntax within your testing framework, for easy reversion.

Solutions: