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Snowflake DEA-C02 Exam Syllabus Topics:

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

1. You are tasked with creating a Snowpark Java stored procedure to calculate a complex, custom rolling average for a time series dataset. This rolling average requires access to external libraries for statistical calculations. Which of the following steps are necessary to successfully deploy and execute this stored procedure?

A) Create a stored procedure in Snowflake, specifying the fully qualified path to the JAR file in the stage, the handler class, and the return type.
B) All of the above.
C) Upload the JAR file to a Snowflake stage.
D) Grant the necessary privileges on the stage and the database to the role executing the stored procedure.
E) Package the Java code and all necessary external libraries into a single JAR file.

2. 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) Monitor resource consumption (CPU, memory, disk) of the virtual warehouse(s) used for processing data from the streams.
B) Regularly query the 'CURRENT_TIMESTAMP and columns of the stream to calculate the data latency.
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) 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.

3. 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 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.
B) Create a masking policy that uses the CURRENT_USER() function to check if the current user belongs to the 'ANALYST' role.
C) 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.
D) 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.
E) 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.

4. Consider a scenario where you have a large dataset of sensor readings stored in a Snowflake table called 'SENSOR DATA'. You need to build an external function to perform complex calculations on these readings using a custom Python library hosted on AWS Lambda'. The calculation requires significant computational resources, and you want to optimize the data transfer between Snowflake and the Lambda function. The following SQL is provided: CREATE OR REPLACE EXTERNAL FUNCTION ARRAY) RETURNS ARRAY VOLATILE MAX BATCH ROWS = 2000 RETURNS NULL ON NULL INPUT API INTEGRATION = aws_lambda_integration AS 'arn:aws:lambda:us-east-1:123456789012:function:sensorProcessor'; Which of the following options would further optimize the performance and reduce data transfer costs, assuming the underlying Lambda function is correctly configured and functional?

A) Convert the input data to a binary format (e.g., using 'TO_BINARY and FROM_BINARY' functions in Snowflake) before sending it to the Lambda function, and decode it in Lambda to reduce the size of the data being transmitted.
B) Compress the data before sending it to the external function and decompress it within the Lambda function. Update the Lambda function to compress the array of results before sending it back to Snowflake and use Snowflake+s functions to decompress it.
C) Reduce the number of columns passed to the external function by performing pre-aggregation or filtering on the data within Snowflake before calling the function.
D) Increase the 'MAX BATCH ROWS' parameter to the maximum allowed value to send larger batches of data to the external function. Ensure Lambda function memory is increased appropriately.
E) Rewrite the custom Python library in Java and create a Snowflake User-Defined Function (UDF) instead of using an external function.

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 '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.
B) 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.
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 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.
E) 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.

Solutions: