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New Snowflake DEA-C02 Exam Question | DEA-C02 Premium Exam

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q254-Q259):

NEW QUESTION # 254
Consider a scenario where you have a Snowflake external table 'ext_logs' pointing to log files in an S3 bucket. The log files are continuously being updated, and new files are added frequently. You want to ensure that your external table always reflects the latest data available in S3. Which of the following actions and configurations are required or recommended to keep the external table synchronized with the underlying data source? (Select all that apply)

A. Implement a stored procedure that periodically executes a query on the external table to force a metadata refresh.
B. Periodically execute the 'ALTER EXTERNAL TABLE ext_logs REFRESH' command to update the metadata about the files in S3.
C. Create a Snowpipe that continuously loads data from the S3 bucket into a Snowflake table instead of using an external table.
D. Enable auto-refresh on the external table using the 'AUTO_REFRESH = TRUE parameter during creation.
E. Configure an event notification service (e.g., AWS SQS) to trigger an external table refresh whenever new files are added to S3.

Answer: B,D,E

Explanation:
Enabling 'AUTO_REFRESH = TRUE (A) automatically refreshes the metadata. Executing 'ALTER EXTERNAL TABLE . REFRESH' (B) manually updates the metadata. Configuring event notifications (C) allows near real-time updates when new files are added. Snowpipe loads data into a Snowflake table, not an external table (D). A stored procedure executing queries doesn't guarantee metadata refresh (E).

NEW QUESTION # 255
You have a Snowflake table named 'ORDERS' with columns 'ORDER D', 'CUSTOMER D', and 'ORDER JSON' (a variant column storing order details). You need to extract specific product names from the 'ORDER JSON' column for each order and return them as a table. The 'ORDER JSON' structure is an array of objects, where each object represents a product with fields like 'product_name' and 'quantity'. Which approach is the most efficient and scalable way to achieve this, considering the possibility of millions of rows in the 'ORDERS table?

A. Create a Java UDF that takes ORDER JSON' as input, parses it using a JSON library, extracts the product names, and returns a comma-separated string. Use a WHILE loop within the UDF to parse the JSON array.
B. Create a Python UDTF that takes 'ORDER JSON' as input, parses it, and yields a row for each product name extracted. Use LATERAL FLATTEN within the UDTF for optimized JSON processing.
C. Create a SQL UDF that iterates through the JSON array using SQL commands and returns a comma-separated string of product names. Then, use SPLIT TO_TABLE to convert the string to rows.
D. Create a JavaScript UDF that takes ' ORDER_JSON' as input and returns an array of product names. Use 'JSON.parse()' to parse the JSON string and iterate using array methods.
E. Use a standard SQL query with LATERAL FLATTEN and JSON VALUE functions to extract product names directly without using a UDF or UDTF.

Answer: B

Explanation:
Python UDTFs with LATERAL FLATTEN are the most efficient and scalable for JSON parsing in Snowflake due to their ability to leverage Snowflake's vectorized execution engine. Using LATERAL FLATTEN inside the UDTF avoids unnecessary data movement and allows for optimized JSON processing. SQL UDFs, Java UDFs, JavaScript UDFs and SQL with JSON_VALUE are generally less performant for complex JSON parsing at scale compared to Python UDTFs leveraging the Snowflake engine.

NEW QUESTION # 256
A data engineering team is loading a large fact table 'SALES DATA' daily, partitioned by 'SALE DATE. After several months, query performance degrades significantly. An analyst reports that queries filtering on 'CUSTOMER are slow, despite 'CUSTOMER ID' having high cardinality. The table definition is as follows: CREATE TABLE SALES_DATA ( SALE DATE DATE NOT NULL, CUSTOMER_ID NUMBER NOT NULL, PRODUCT ID NUMBER NOT NULL, SALE_AMOUNT ... Which of the following actions would BEST improve query performance for queries filtering on 'CUSTOMER ID', considering the existing partitioning by 'SALE DATE'?

A. Cluster the 'SALES DATA' table on 'CUSTOMER ID.
B. Increase the virtual warehouse size.
C. Create a secondary index on 'CUSTOMER ID'
D. Create a materialized view that aggregates data by 'CUSTOMER_ID and relevant dimensions.
E. Partition the table by 'CUSTOMER_ID instead of 'SALE_DATE.

Answer: A

Explanation:
Clustering the table on 'CUSTOMER_ID' will physically organize the data based on this column, improving the performance of queries filtering on 'CUSTOMER ID. While increasing warehouse size (E) might provide some performance boost, clustering addresses the underlying data organization issue. Secondary indexes (A) are not supported in Snowflake. Partitioning by 'CUSTOMER_ID (D) isn't possible in Snowflake. Materialized views (B) are a valid option for pre-aggregation, but clustering will directly improve base table performance for filtering. Therefore, clustering is the best option.

NEW QUESTION # 257
You have a 'WEB EVENTS' table that stores user activity on a website. It includes columns like 'USER ID, 'EVENT TYPE , EVENT TIMESTAMP, and 'PAGE URL'. You need to create a materialized view that calculates the number of distinct users visiting each page daily. You are also tasked with minimizing the impact on the underlying 'WEB EVENTS' table during materialized view refreshes, as other critical processes rely on it. Which of the following strategies would provide the MOST efficient solution, considering both performance and concurrency?

A. Create a task that truncates and reloads the materialized view daily. This ensures data consistency and prevents incremental refresh issues.
B. Create a materialized view and schedule regular, small batch refreshes to minimize lock contention and resource consumption on the 'WEB_EVENTS' table.
C. Create a materialized view and configure it to incrementally refresh, leveraging Snowflake's automatic refresh capabilities without any explicit scheduling.
D. Create a standard materialized view that calculates the distinct user count per page daily directly from the 'WEB EVENTS table without any special configuration.
E. Create a materialized view with a 'REFRESH COMPLETE strategy to ensure full data consistency after each refresh, even though it may lock the underlying table.

Answer: C

Explanation:
Option D provides the most efficient solution. Incremental refreshes are designed to efficiently update the materialized view with only the changes from the base table, minimizing the impact on the 'WEB_EVENTS' table. Options A, B and E might lock the table for longer periods. Option C is not a valid option in Snowflake. Option E is not efficient since it involves truncating and reloading the materialized view, consuming unnecessary resources and being potentially slow.

NEW QUESTION # 258
You have implemented a masking policy on the 'EMAIL' column of a 'USERS' table. The policy masks the email address for all users except those with the 'SUPPORT' role. You now need to grant the 'SELECT' privilege on this table to a new role, 'ANALYST. You want to ensure that the masking policy continues to work as expected for the 'ANALYST' role. Which of the following SQL statements should you execute?

Answer: D

Explanation:
Masking policies in Snowflake are automatically applied when a user queries a table with a column that has a masking policy set. Granting 'SELECT privilege to the 'ANALYST role does not require any further action related to the masking policy. The masking policy will automatically be applied based on the role of the user executing the query. Options B, C and E involve unnecessary or incorrect steps after granting the select privilege.

NEW QUESTION # 259
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