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

1. You are tasked with implementing row-level security (RLS) on a 'SALES' table to restrict access based on the 'REGION' column. Users with the 'NORTH REGION ROLE should only see data where 'REGION = 'NORTH". You've created a row access policy named north_region_policy'. After applying the policy to the 'SALES table, users with the 'NORTH REGION ROLE are still seeing all rows.
Which of the following is the MOST likely reason for this and how can it be corrected?

A) The is not enabled. Execute 'ALTER ROW ACCESS POLICY ON SALES SET ENABLED = TRUE;'
B) The user has not logged out and back in since the role was granted to them. Force the user to re-authenticate.
C) The policy function within is not using the correct context function to determine the user's role. It should use 'CURRENT ROLE()' instead of 'CURRENT_USER()'
D) The policy needs to be explicitly refreshed. Execute 'REFRESH ROW ACCESS POLICY north_region_policy ON SALES;'
E) The ' does not have the USAGE privilege on the database and schema containing the 'SALES' table. Grant the USAGE privilege to the role.

2. You have configured a Snowpipe to load data from an AWS S3 bucket into a Snowflake table. The data in S3 is updated frequently. You've noticed that despite the Snowpipe being active and the S3 event notifications being configured correctly, some newly added files are not being picked up by the Snowpipe. You run 'SYSTEM$PIPE and see the 'executionstate' is 'RUNNING' but the 'pendingFileCount' remains at O, even after new files are placed in the S3 bucket. Choose all of the reasons that could explain the observations.

A) There is an insufficient warehouse size configured for the Snowpipe. Increase the warehouse size for optimal performance.
B) The IAM role associated with your Snowflake account does not have sufficient permissions to read from the S3 bucket. Specifically, it lacks the 's3:GetObject' permission.
C) The SQS queue or SNS topic associated with the S3 event notifications has a message retention period that is too short. Messages containing event details for new files are being deleted before Snowpipe can process them.
D) The S3 event notification configuration is missing the 's3:ObjectCreated: event type, meaning that new file creation events are not being sent to the SQS queue or SNS topic.
E) The file format specified in the Snowpipe definition does not match the actual format of the files being placed in the S3 bucket.

3. A data provider wants to share a large dataset (several TB) with multiple consumers. The dataset is updated daily. The provider wants to minimize the cost associated with data sharing and ensure that consumers receive consistent data'. Which of the following strategies would be the MOST cost-effective and maintainable?

A) Create a data share with views that point to the base tables. Use time travel to allow consumers to query data from a specific point in time before the daily update.
B) Create a data share and create a separate database for each consumer, cloning the data into each consumer's database daily.
C) Create a data share and grant access to all consumers directly on the base tables.
D) Create a data share with views that point to the base tables, and clone the base tables daily into a separate 'staging' database before sharing.
E) Create a data share containing external tables pointing to data stored in cloud storage (e.g., AWS S3), updated daily using a pipeline.

4. A data engineer is working with a Snowpark DataFrame 'sales df containing sales data with columns 'product id', 'sale_date', and 'sale amount'. The engineer needs to calculate the cumulative sales amount for each product over time. Which of the following code snippets using window functions correctly calculates the cumulative sales amount, ordered by 'sale date'?

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

5. You have a Snowflake table 'ORDERS' with billions of rows storing order information. The table includes columns like 'ORDER ID', 'CUSTOMER ID', 'ORDER DATE, 'PRODUCT_ID', and 'ORDER AMOUNT'. Analysts frequently run queries filtering by 'ORDER DATE' and 'CUSTOMER ID to analyze customer ordering trends. The performance of these queries is slow. Assuming you've already considered clustering and partitioning, which of the following strategies would BEST improve query performance, specifically targeting these filtering patterns? Assume the table is large enough for search optimization to be beneficial.

A) Create a materialized view that pre-aggregates the data based on 'ORDER_DATE and "CUSTOMER_ID
B) Enable search optimization on the 'PRODUCT ID column.
C) Enable search optimization on both the 'ORDER DATE and 'CUSTOMER IDS columns.
D) Enable search optimization on the 'ORDER_ID column.
E) Enable search optimization on the 'ORDER_DATE' column.

Solutions: