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

1. A data engineering team is responsible for an ELT pipeline that loads data into Snowflake. The pipeline has two distinct stages: a high- volume, low-complexity transformation stage using SQL on raw data, and a low-volume, high-complexity transformation stage using Python UDFs that leverages an external service for data enrichment. The team is experiencing significant queueing during peak hours, particularly impacting the high-volume stage. You need to optimize warehouse configuration to minimize queueing. Which combination of actions would be MOST effective?

A) Create a single, large (e.g., X-Large) warehouse and rely on Snowflake's automatic scaling to handle the workload.
B) Create two separate warehouses: a Large, multi-cluster warehouse configured for auto-scale for the high-volume, low-complexity transformations and a Small warehouse for the low-volume, high-complexity transformations.
C) Create a single, X-Small warehouse and rely on Snowflake's query acceleration service to handle the workload.
D) Create two separate warehouses: a Medium warehouse for the high-volume, low-complexity transformations and an X-Small warehouse for the low-volume, high-complexity transformations.
E) Create two separate warehouses: a Small warehouse configured for auto-suspend after 5 minutes for the high-volume, low-complexity transformations and a Large warehouse configured for auto-suspend after 60 minutes for the low-volume, high-complexity transformations.

2. A financial services company stores sensitive customer data, including credit card numbers, in a Snowflake table called 'CUSTOMER DATA. You need to implement dynamic data masking on the 'CREDIT CARD NUMBER column. You want to ensure that only users with the FINANCE ADMIN' role can view the unmasked credit card numbers. All other users should see a masked version of the data'. Which of the following set of commands is the MOST efficient and secure way to achieve this?

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

3. You are tasked with creating a Python script to load data from a CSV file stored in an AWS S3 bucket into a Snowflake table. You have the following requirements: 1. Use the 'COPY INTO' command for efficient data loading. 2. Handle potential schema evolution in the CSV file (e.g., new columns being added). 3. Automatically create the target table if it doesn't exist, inferring the schema from the CSV. Which combination of Snowflake Python connector functionalities and 'COPY INTO' options would best address these requirements, assuming you have an AWS IAM role configured for Snowflake access to S3?

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

4. 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 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.
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 overly complex and contains computationally expensive functions. Simplify the policy logic and avoid using UDFs or complex subqueries within the policy definition.
D) 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.
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.

5. A data engineering team is implementing column-level security on a Snowflake table named 'CUSTOMER DATA containing sensitive PII. They want to mask the 'EMAIL' column for users in the 'ANALYST role but allow users in the 'DATA SCIENTIST role to view the unmasked email addresses. The 'ANALYST role already has SELECT privileges on the table. Which of the following steps are necessary to achieve this using a masking policy?

A) Create a dedicated view on 'CUSTOMER DATA' for analysts with the 'EMAIL' column masked using a CASE statement within the view's SELECT statement. Grant SELECT privilege to the ANALYST role on the view only.
B) Create a masking policy with a CASE statement that checks the CURRENT ROLE() function to see if it's 'ANALYST'. If true, mask the email; otherwise, return the original email.
C) Create a masking policy that uses the CURRENT ROLE() function to return a masked value if the current role is 'ANALYST and the original value otherwise.
D) Create a masking policy that uses the IS_ROLE_IN_SESSION('ANALYST') function to return a masked value if the analyst role is active in current session and the original value otherwise.
E) Create a masking policy that uses the CURRENT_USER() function to check if the current user belongs to the 'ANALYST' role.

Solutions: