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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You have a Snowflake table called 'ORDERS' with a column named 'ORDER DETAILS that contains JSON data'. You need to create a UDF to extract a specific value from the JSON and return it. The JSON structure is consistent: '{'customer': {'name': 'John Doe', 'email': '[email protected]'}, 'items': [{'item id': 1, 'quantity': 2}, {'item id': 2, 'quantity': You want to extract the customer's email address. Which of the following UDF definitions is the most efficient and correct?

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

2. You are implementing Snowpipe using the REST API for a custom data ingestion process. Your application uploads data files to an internal stage named '@MY STAGE and then calls the Snowpipe REST API to trigger the data load. However, you are encountering 'insufficient privileges' errors when calling the API, even though the role used to authenticate the API requests has the 'USAGE' privilege on the stage and the 'OPERATE' privilege on the pipe. The Pipe name is 'MY PIPE'. Which of the following is the MOST LIKELY cause of this error, and what can you do to resolve it?

A) The role used to authenticate the API requests requires the 'INSERT privilege on the target table in addition to 'USAGE on the stage and 'OPERATE on the pipe. Grant the 'INSERT privilege to the role.
B) The role used to authenticate the API requests does not have the 'OWNERSHIP' privilege on the Snowpipe.
C) The Snowpipe's 'EXECUTE TASK' privilege has not been granted to the role used for the REST API calls. Grant the 'EXECUTE TASK' privilege to the role.
D) The role used to authenticate the API requests does not have the 'SELECT' privilege on the underlying table that the Snowpipe is loading data into.
E) The role doesn't have the 'USAGE' privilege on the database and schema containing the stage and pipe. Grant 'USAGE' on the database and schema.

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) 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.
C) Issue a 'ALTER DATABASE REFRESH FROM command. This command automatically handles schema changes and ensures data consistency with minimal downtime.
D) Issue a 'ALTER REPLICATION GROUP REFRESH$ command. This will automatically handle schema changes and resume replication.
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. A data engineering team is tasked with optimizing a complex query that joins three tables: 'ORDERS' , 'CUSTOMERS' , and 'PRODUCTS. The 'ORDERS' table contains millions of records and is frequently joined with 'CUSTOMERS' (containing customer demographics) and 'PRODUCTS' (containing product details). The initial query uses standard JOIN syntax, but performance is slow. The query retrieves order details along with customer and product information, filtering by a specific date range in the 'ORDERS' table and a customer segment in the 'CUSTOMERS table. Which optimization strategy would be MOST effective for significantly improving query performance?

A) Increase the virtual warehouse size to X-LARGE without analyzing the query profile.
B) Create materialized views that pre-join the 'ORDERS', 'CUSTOMERS, and 'PRODUCTS tables and filter based on common criteria.
C) Apply clustering keys to the 'ORDERS table based on the date column used in the WHERE clause and clustering keys to the 'CUSTOMERS' table on the customer segment column. Also create appropriate indexes.
D) Replace the standard JOINs with LATERAL FLATTEN operations.
E) Convert the entire dataset into a single VARIANT column and query using JSON path expressions.

5. You are tasked with creating a UDTF in Snowflake to perform a complex data transformation that requires external libraries (e.g., for advanced string manipulation or data analysis). The transformation involves cleaning and standardizing addresses from a table containing millions of customer records. Which language and approach would be most appropriate and efficient for this scenario?

A) Python UDTF leveraging Anaconda packages (e.g., 'addressparser' , 'pandas') for advanced address parsing and standardization, utilizing Snowflake's optimized execution environment for Python.
B) SQL UDF with nested CASE statements for address standardization.
C) Scala UDTF leveraging sbt to manage dependencies to achieve address parsing and standardization.
D) JavaScript UDF utilizing regular expressions for simple string replacements.
E) Java UDTF with necessary JAR files uploaded to Snowflake's internal stage, leveraging external libraries for address parsing and standardization.

Solutions: