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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are troubleshooting a slow-running query that joins a large fact table 'SALES DATA' (100 billion rows) with a smaller dimension table 'CUSTOMER DIM' (1 million rows) on 'CUSTOMER ID. Initial analysis shows that the query is spending significant time in the join operation. You suspect the issue lies with the join strategy being used by Snowflake. Which of the following actions are MOST likely to improve query performance and optimize the join?

A) Increase the virtual warehouse size and monitor for spillover to local disk. If spilling occurs, further increase the warehouse size.
B) Analyze the query profile in Snowflake's web UI and identify if a broadcast join is occurring. If so, consider increasing session parameter (within limits) or re-designing the query to avoid the broadcast join.
C) Ensure both 'SALES DATA' and 'CUSTOMER DIM' are clustered on 'CUSTOMER ID.
D) Ensure that the 'CUSTOMER_ID column in both tables has compatible datatypes and that no implicit type conversions are happening during the join. Also check cardinality of 'CUSTOMER_ID in the SALES DATA table.
E) Convert the query to use a LATERAL FLATTEN function to pre-process the 'CUSTOMER_DIW table before the join.

2. You need to unload data from a Snowflake table named 'CUSTOMER DATA to an AWS S3 bucket The data should be unloaded in Parquet format, partitioned by the 'CUSTOMER REGION' column, and automatically compressed with GZIP. Furthermore, you only want to unload customers whose 'REGISTRATION DATE is after '2023-01-01'. Which of the following 'COPY INTO' statement correctly achieves this?

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

3. You have data residing in AWS S3 in Parquet format, which is updated daily with new columns being added occasionally. The data is rarely accessed, but when it is, it needs to be queried using SQL within Snowflake. You want to minimize storage costs within Snowflake while ensuring the data can be queried without requiring manual table schema updates every time a new column is added to the S3 data'. Which approach is MOST suitable?

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

4. You are working with a Snowflake table 'customer_data' which contains customer information stored in a VARIANT column named raw_info'. The 'raw_info' JSON structure includes nested addresses, and preferences. Your task is to extract the city from the first address in the 'addresses' array, and the customer's preferred communication method from the 'preferences' object. Some customers might not have addresses or preferences defined. Select the two SQL snippets that correctly and efficiently extract this data, handling missing fields gracefully and providing appropriate type casting. Address array is in the format 'addresses: [ { 'city': '...', 'state': ' '},

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

5. You have implemented a row access policy on a 'products' table to restrict access based on the user's group. The policy uses a mapping table 'user_groups' to determine which products a user is allowed to see. After implementing the policy, users are reporting significant performance degradation when querying the 'products' table. What are the MOST likely causes of this performance issue, and what steps can you take to mitigate them? Select all that apply.

A) The users do not have sufficient privileges to access the 'user_groups' table. Grant the necessary SELECT privileges to the users on the 'user_groupS table.
B) The 'user_groups' table is not properly indexed, causing slow lookups during policy evaluation. Create an index on the 'username' and 'group' columns of the 'user_groups' table.
C) The row access policy is interfering with Snowflake's data pruning capabilities. Ensure that the policy expression can be evaluated efficiently by Snowflake's query optimizer by using the 'USING' clause of the ROW ACCESS POLICY.
D) The row access policy is overly complex and contains computationally expensive functions. Simplify the policy logic and avoid using UDFs or complex subqueries within the policy definition.
E) The row access policy is causing full table scans on the 'products' table. Review the query patterns and consider adding clustering keys to the 'products' table to improve data access patterns.

Solutions: