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

1. You are tasked with optimizing a continuous data pipeline that loads data from an external stage into a Snowflake table using streams.
The pipeline is experiencing significant latency during peak hours. The stream is defined on a very large table with frequent updates and deletes. Which of the following strategies would be MOST effective in reducing the latency of the data pipeline, considering stream performance and cost implications?

A) Increase the size of the virtual warehouse used for loading data. This will provide more compute resources for processing the stream.
B) Implement a materialized view on top of the stream to pre-aggregate the data.
C) Implement a more aggressive pruning strategy on the base table to reduce the amount of data that the stream needs to track.
D) Reduce the RETENTION TIME of the stream. This will limit the amount of historical data tracked and improve performance.
E) Create multiple streams on the same base table, each filtering for specific types of changes (e.g., INSERT, UPDATE, DELETE).

2. You are responsible for monitoring a critical data pipeline that loads data from an external Kafka topic into a Snowflake table 'ORDERS' Data anomalies have been frequently observed, impacting downstream reporting. You want to implement a solution that proactivelyidentifies and alerts on data quality issues such as missing values, invalid formats, and unexpected data distributions. Which combination of Snowflake features and approaches would be MOST effective for achieving this objective with minimal performance overhead on the pipeline itself?

A) Employing Snowflake's built-in statistics and histogram features to analyze data distribution in the 'ORDERS' table and configure alerts based on deviations from historical patterns, combined with a Snowflake Native App for data quality reporting.
B) Creating a separate Snowflake pipeline that reads from the same Kafka topic, performs data quality checks in real-time using Snowpipe and streams the results to an alert system.
C) Using Snowflake's 'VALIDATE' table function after the data load to check for data corruption and then trigger alerts based on the validation results.
D) Leveraging Snowflake's Data Governance features along with Snowpark UDFs to define and enforce data quality rules at the time of ingestion using a Python- based library like Great Expectations, configured to trigger alerts through Snowflake Notifications.
E) Implementing custom SQL-based data quality checks within a scheduled Snowflake task that runs after the data load and writing results to an audit table for monitoring.

3. You are developing a Secure UDF in Snowflake to encrypt sensitive customer data'. The UDF should only be accessible by authorized roles. Which of the following steps are essential to properly secure the UDF?

A) Ensuring that the UDF is owned by a role with appropriate permissions and limiting access to this role.
B) Using masking policies instead of Secure UDFs is the recommended approach for data security
C) Granting the EXECUTE privilege on the UDF only to the roles that require access.
D) Using the 'SECURE keyword when creating the UDF to prevent viewing the UDF definition.
E) Setting the 'SECURITY INVOKER clause when creating the UDF to execute the UDF with the privileges of the caller.

4. You are working with a very large Snowflake table named 'CUSTOMER TRANSACTIONS which is clustered on 'CUSTOMER ID and 'TRANSACTION DATE. After noticing performance degradation on queries that filter by 'TRANSACTION AMOUNT and 'REGION' , you decide to explore alternative clustering strategies. Which of the following actions, when performed individually, will LEAST likely improve query performance specifically for queries filtering by 'TRANSACTION AMOUNT and 'REGION', assuming you can only have one clustering key?

A) Creating a new table clustered on 'TRANSACTION_AMOUNT and 'REGION', and migrating the data.
B) Creating a search optimization on 'TRANSACTION_AMOUNT' and 'REGION' columns.
C) Dropping the existing clustering key and clustering on 'TRANSACTION_AMOUNT' and 'REGION'.
D) Adding ' TRANSACTION_AMOUNT and 'REGIO!V to the existing clustering key while retaining 'CUSTOMER_ID and 'TRANSACTION_DATE
E) Creating a materialized view that pre-aggregates data by 'TRANSACTION_AMOUNT and 'REGION'.

5. You are responsible for monitoring the performance of several data pipelines in Snowflake that heavily rely on streams. You notice that some streams consistently lag behind the base tables. You need to proactively identify the root cause and implement solutions. Which of the following metrics and monitoring techniques would be MOST helpful in diagnosing and resolving the stream lag issue? (Select all that apply)

A) Monitor the 'SYSTEM$STREAM HAS DATA function's output for the affected streams to quickly determine if there are pending changes.
B) Analyze the query history in Snowflake to identify any long-running queries that are consuming data from the streams and potentially blocking new changes from being processed.
C) Regularly query the 'CURRENT_TIMESTAMP and columns of the stream to calculate the data latency.
D) Increase the 'DATA RETENTION TIME IN DAYS for the base tables to ensure that historical data is always available for the streams, even if they lag behind.
E) Monitor resource consumption (CPU, memory, disk) of the virtual warehouse(s) used for processing data from the streams.

Solutions: