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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. Your company is implementing data governance policies in Snowflake and wants to automatically classify data to track Personally Identifiable Information (PII). You have defined a classification policy with a tag 'PII' and associated tag values like 'Email', 'CreditCard', and 'SSN'. You want to monitor the usage of PII data'. Which of the following approaches is the MOST efficient way to track access and modifications to columns tagged with the 'PII' tag and any of its tag values?

A) Enable Snowflake's data governance features, including object tagging and data classification. Then, leverage the ACCESS HISTORY view, filtering based on POLICY _ TAGS. This will show all accesses to data tagged as PII.
B) Implement a custom UDF that intercepts all queries. Inside the UDF, check if any of the accessed tables or columns are tagged with 'PII', and log those queries into a separate logging table for monitoring.
C) Create a scheduled task to query the INFORMATION SCHEMCOLUMNS view and filter based on the TAG DATABASE, TAG SCHEMA, and TAG NAME columns to identify PII columns, then query the QUERY_HISTORY view and filter based on the identified column names.
D) Use Snowflake's native data governance capabilities in conjunction with Snowflake Horizon to actively monitor data quality and access patterns, specifically focusing on PII-tagged columns.
E) Create a masking policy that redacts all PII data. Monitor access attempts via the ACCESS HISTORY view. Although it hinders data usability, this approach guarantees no PII data leakage and allows easy monitoring.

2. A Snowflake data pipeline ingests data from multiple external sources into a RAW DATA table. A transformation process then moves the data to a ANALYTICS DATA table, applying several complex UDFs written in Java and Python for data cleansing and enrichment. Performance is significantly slower than expected. Which combination of techniques would BEST improve the performance of this transformation pipeline?

A) Implement data partitioning in the RAW DATA table based on ingestion time and switch to using stored procedures instead of transformation pipelines.
B) Reduce the number of UDF calls by consolidating them into a single, more complex UDF. Replace the transformation pipeline with a series of COPY INTO statements.
C) Use external functions instead of UDFs to offload the processing to an external compute environment and configure auto-scaling for the virtual warehouse.
D) Increase the virtual warehouse size and re-cluster the ANALYTICS DATA table based on the most frequently filtered columns.
E) Rewrite the UDFs in SQL or Snowpark Python/Java for better integration with the Snowflake engine and leverage vectorization where possible; cache intermediate results using temporary tables.

3. You have configured a replication group to replicate a database 'CUSTOMER DATA' from your primary Snowflake account (AWS us- east-I) to your secondary Snowflake account (Azure eastus). After a recent network outage, the replication process stopped. Upon investigation, you find that some tables in the 'CUSTOMER DATA' database in the primary account have been modified (schema changes). You need to resume replication and ensure data consistency in the secondary account, with minimal impact on users querying the secondary database. Which of the following commands or sequence of commands would be the MOST appropriate to refresh the secondary database while minimizing downtime for query users?

A) First, suspend the replication group with 'ALTER REPLICATION GROUP SUSPEND;'. Then, drop the secondary database 'DROP DATABASE . Finally, recreate the secondary database as a replica: 'CREATE DATABASE AS REPLICA OF and resume the replication group 'ALTER REPLICATION GROUP RESUME;'.
B) Issue a 'ALTER DATABASE REFRESH FROM command. This command automatically handles schema changes and ensures data consistency with minimal downtime.
C) Issue a 'ALTER REPLICATION GROUP REFRESH$ command. This will automatically handle schema changes and resume replication.
D) First, pause all running queries on the secondary account. Then, execute a full refresh using 'ALTER DATABASE REFRESH FROM Finally, resume the queries on the secondary account.
E) Suspend the replication group with 'ALTER REPLICATION GROUP SUSPEND;' Then execute the following: 'ALTER DATABASE ENABLE REPLICATION TO ACCOUNT ALTER REPLICATION GROUP RESUME;'

4. 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) Regularly query the 'CURRENT_TIMESTAMP and columns of the stream to calculate the data latency.
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) Monitor the 'SYSTEM$STREAM HAS DATA function's output for the affected streams to quickly determine if there are pending changes.
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.

5. A Snowflake data engineer is troubleshooting a performance issue with a query that retrieves data from a large table (TRANSACTIONS). The table has a VARIANT column containing semi-structured JSON data representing transaction details. The query uses several LATERAL FLATTEN functions to extract specific fields from the JSON and filters the data based on these extracted values. Despite having adequate virtual warehouse resources, the query is running slower than expected. Identify the MOST effective strategy to improve the performance of this query:

A) Increase the virtual warehouse size to provide more memory for processing the JSON data.
B) Create a search optimization service on the TRANSACTIONS table for the VARIANT column.
C) Create a new table with pre-extracted fields from the VARIANT column and use this table in the query instead of the LATERAL FLATTEN operations.
D) Rewrite the query to use regular expressions instead of LATERAL FLATTEN for extracting the fields from the JSON data.
E) Convert the VARIANT column to a VARCHAR column and store the JSON data as a string.

Solutions: