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Snowflake DEA-C02 Exam Syllabus Topics:

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

Question 1

You are developing a data pipeline that uses Snowpipe Streaming to ingest JSON data into a Snowflake table. Some JSON documents contain nested arrays and complex structures. You need to flatten the JSON structure during ingestion to simplify querying. Consider the following JSON document: { "order id": 123, "customer": { "id": "cust123", "name": "John Doe", "address": { "street": "123 Main St", "city": "Anytown" } }, "items": [ {"product_id": "prodl", "quantity": 2}, {"product_id": "prod2", "quantity": 1} ] } Which approach would you use within the 'COPY INTO' statement of your Snowpipe to flatten this JSON structure during ingestion?

A. Create a separate transformation pipeline using Snowflake Tasks to flatten the data after it is ingested into the table.
B. Use the ' FLATTEN()' table function directly within the 'COPY INTO' statement to expand the 'items' array and extract nested fields. For nested objects, use dot notation directly in the SELECT list (e.g., 'customer.name').
C. Use JavaScript UDFs within the 'COPY INTO' statement to recursively flatten the JSON structure.
D. Pre-process the JSON documents before loading them into the stage using a custom script to flatten the structure.
E. Snowpipe and the 'COPY INTO' command automatically flattens JSON data during ingestion; no additional steps are required.

Question 2

A data engineer is investigating high credit consumption on a Snowflake warehouse due to frequent re-clustering operations on a large table named 'WEB EVENTS. This table is clustered on 'EVENT TIMESTAMP' and 'USER ID. The engineer suspects that the high frequency of data ingestion, especially out-of-order 'EVENT TIMESTAMP' values, contributes to the poor clustering. Choose the options that can lead to optimizing clustering and reducing credit consumption, assuming you have limited control over the ingestion process and data quality.

A. Implement a maintenance task to periodically re-cluster the table less frequently, but at more strategically chosen times (e.g., during off-peak hours).
B. Partition the table based on "EVENT _ TIMESTAMP' instead of clustering.
C. Drop the clustering key altogether to avoid re-clustering costs.
D. Implement a pre-processing stage to sort the incoming data by 'EVENT TIMESTAMP before loading it into the 'WEB EVENTS table, using a temporary table and then inserting into the final table.
E. Increase the warehouse size to accelerate the re-clustering process.

Question 3

A financial institution needs to mask sensitive customer data (PII) in a 'CUSTOMER' table. The table contains columns like 'CUSTOMER ID', 'FIRST NAME', 'LAST NAME', 'CREDIT CARD, and 'ADDRESS'. The data should be masked differently for different roles: 'ANALYST' role should see obfuscated values for names and addresses, while the 'SUPPORT' role should see the last four digits of the credit card and a hashed version of the address. The "CUSTOMER ID' should never be masked. Assume a central masking policy already exists called 'PII MASKING POLICY. Which of the following statements is the MOST efficient and secure way to achieve this?

A. Create multiple masking policies, one for each role and sensitive column combination, each with the appropriate masking expression. Then, apply each masking policy individually to its respective column. Use the function to implement role-based masking within each policy.
B. Create multiple masking policies with different masking expressions and apply them directly to the columns based on the role using conditional expressions within the policies. Use 'CASE statements within the masking policy to differentiate between roles.
C. Create external functions to handle the complex masking logic and call them from the masking policy.
D. Create view for each role which applies masking functions to the columns. Grant SELECT access on those views to relevant roles.
E. Create a single masking policy with a complex stored procedure that checks the current role and applies different masking functions accordingly, then apply this policy to all sensitive columns.

Question 4

A large e-commerce company is experiencing performance issues with its daily sales report queries. These queries aggregate data from a fact table 'SALES FACT (100 billion rows) and several dimension tables, including 'CUSTOMER DIM', 'PRODUCT DIM', and 'DATE DIM'. The queries are run every morning and are essential for business decision-making. The team has identified that the 'SALES FACT table's primary key is 'SALE ID, but the queries frequently filter and join on 'CUSTOMER and 'PRODUCT ID. You want to use query acceleration service for these reports without changing query logic. Which combination of actions will MOST effectively leverage query acceleration service, assuming sufficient credits?

A. Enable search optimization on the columns 'CUSTOMER ID' and 'PRODUCT ID of the 'SALES FACT table, then enable query acceleration on the virtual warehouse. Set the QUERY_ACCELERATION_MAX_SCALE_FACTOR parameter to a reasonable value based on testing.
B. Create materialized views that pre-aggregate the sales data based on 'CUSTOMER ID', 'PRODUCT ID, and 'DATE ID, then enable query acceleration on the virtual warehouse.
C. Enable Automatic Clustering on the 'SALES FACT table based on 'CUSTOMER ID' and 'PRODUCT ID, then enable query acceleration on the virtual warehouse.
D. Increase the size of the virtual warehouse used for running the reports and enable query acceleration. Set the parameter to a high value.
E. Enable clustering on the 'CUSTOMER DIM' and 'PRODUCT DIMS tables.

Question 5

You have created a secure external function that uses a Snowflake secret to retrieve a cryptographic key and mask sensitive data'. However, users are still able to see the sensitive data'. Which of the following actions is MOST likely to resolve the issue?

A. Ensure that the user calling the function has the 'USAGE privilege on the security integration associated with the external function.
B. Verify that the user has the 'OWNERSHIP privilege on the table containing the sensitive data.
C. Grant the 'EXECUTE FUNCTION' privilege on the function to the user.
D. Grant the ' SELECT privilege on the Snowflake secret object to the user.
E. Ensure that the user has the 'USAGE privilege on the database and schema where the secret is stored.

Solutions: