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Snowflake DEA-C02 Exam Consultant & DEA-C02 Reliable Dumps Questions

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q219-Q224):

NEW QUESTION # 219
You are developing a Snowpark Python stored procedure that performs complex data transformations on a large dataset stored in a Snowflake table named 'RAW SALES'. The procedure needs to efficiently handle data skew and leverage Snowflake's distributed processing capabilities. You have the following code snippet:

Which of the following strategies would be MOST effective to optimize the performance of this Snowpark stored procedure, specifically addressing potential data skew in the 'product id' column, assuming 'product_id' is known to cause uneven data distribution across Snowflake's micro-partitions?

A. Combine salting with repartitioning by adding a random number to the 'product_id' before repartitioning, then removing the salt after the transformation to break up the skew. Then, enable automatic clustering on the 'TRANSFORMED SALES' table.
B. Implement a custom partitioning strategy using before the transformation logic to redistribute data evenly across the cluster.
C. Increase the warehouse size significantly to compensate for the data skew and improve overall processing speed without modifying the partitioning strategy.
D. Utilize Snowflake's automatic clustering on the 'TRANSFORMED_SALES table by specifying 'CLUSTER BY when creating or altering the table to ensure future data is efficiently accessed.
E. Use the 'pandas' API within the Snowpark stored procedure to perform the transformation, as 'pandas' automatically optimizes for data skew.

Answer: A

Explanation:
Option E is the most effective solution. Salting breaks up data skew before repartitioning. Automatic clustering on the transformed table optimizes future queries. Repartitioning redistributes the data across Snowflake's processing nodes, and Automatic Clustering will help in maintaining performance as the data changes in TRANSFORMED_SALES table over time. Option A, without salting, may still be inefficient due to the initial skew. Option B improves query performance but doesn't address the initial transformation skew. Option C is incorrect because 'pandas' in Snowpark does not automatically handle data skew at the Snowflake level. Option D is a costly workaround that doesn't fundamentally solve the skew problem.

NEW QUESTION # 220
A data provider wants to create a Listing in the Snowflake Marketplace. They want to ensure that consumers can only access the data in a secure and controlled manner. The provider needs to restrict data access based on specific roles within the consumer's Snowflake account and track data usage. Which of the following steps are NECESSARY to achieve these requirements?

A. Create a Reader Account and share the data through that account, managing access directly.
B. Implement a secure view with a 'WHERE clause that filters data based on the consumer's context and share the view in the Listing.
C. Grant direct access to the underlying tables and views to the consumer's roles.
D. Implement row-level security policies on the shared tables and views to filter data based on consumer roles. Share the tables and views in the Listing. Monitor usage through Snowflake's account usage views.

Answer: D

Explanation:
Row-level security policies are critical for restricting data access based on consumer roles. Secure views can achieve similar outcomes, but row-level policies provides a more structured and scalable solution. Sharing the tables and views allows consumers to directly query the data, while the security policies enforce the access restrictions. Snowflake's account usage views provide the necessary tools for tracking data usage by consumers. Creating Reader accounts, and not using listings at all are valid approaches but don't make use of Snowflake Listings.

NEW QUESTION # 221
You are tasked with creating a JavaScript stored procedure in Snowflake to perform a complex data masking operation on sensitive data within a table. The masking logic involves applying different masking rules based on the data type and the column name. Which approach would be the MOST secure and maintainable for storing and managing these masking rules? Assume performance is not your primary concern but code reuse and maintainability is the most important thing.

A. Storing masking logic in Javascript UDFs and calling these UDFs dynamically within the stored procedure based on column names and datatype
B. Hardcoding the masking rules directly within the JavaScript stored procedure.
C. Using external stages and pulling the masking rules from a configuration file during stored procedure execution.
D. Storing the masking rules in a separate Snowflake table and querying them within the stored procedure.
E. Defining the masking rules as JSON objects within the stored procedure code.

Answer: A,D

Explanation:
Options B and E are the most secure and maintainable. Storing the masking rules in a separate Snowflake table allows for easy modification and version control without altering the stored procedure code. Javascript UDFs make the logic reusable, maintainable and dynamic. Hardcoding the rules (A) makes maintenance difficult. JSON objects within code (C) are an improvement but are still embedded within the code. Using external stages (D) introduces dependencies and potential security risks if not managed carefully.

NEW QUESTION # 222
A critical database, 'PRODUCTION DB', in your Snowflake account was accidentally dropped. You need to restore it as quickly as possible, but you're unsure if Time Travel retention is sufficient. Which method guarantees restoration of the database even if it falls outside the Time Travel window?

A. Use the 'UNDROP DATABASE PRODUCTION command.
B. Utilize the data cloning feature: 'CREATE DATABASE CLONE PRODUCTION_DB BEFORE (STATEMENT 'DROP DATABASE PRODUCTION_DB');'
C. Contact Snowflake Support and request restoration from Fail-safe.
D. Fail-safe cannot be directly accessed by the user for restoration purposes; it is only used by Snowflake Support in extreme disaster recovery scenarios.
E. Restore from a Snowflake-managed backup using the 'CREATE DATABASE ... FROM BACKUP' command. Specify the timestamp before the drop occurred.

Answer: D

Explanation:
Fail-safe is a last resort for data recovery managed entirely by Snowflake. Users cannot directly access or restore data from Fail- safe. Options B and C are valid for Time Travel, but fail if the data falls outside of that window. Option A is partially correct; you contact Snowflake support, who then might use fail-safe if appropriate, but option E is the most accurate answer. Option D uses Time Travel, which may not work.

NEW QUESTION # 223
You have a Snowflake view that joins three large tables: ORDERS, CUSTOMERS, and PRODUCTS. The query accessing this view is frequently used but performs poorly. You suspect inefficient join processing and potential skew in the data'. Which of the following strategies can be used to optimize the view's performance? (Select all that apply)

A. Replace the view with a materialized view to precompute and store the results.
B. Use JOIN hints, such as BROADCAST or MERGE, to guide the query optimizer on join strategies.
C. Partition the underlying tables based on the join keys to improve data locality.
D. Analyze the query profile to identify bottlenecks and potential data skew issues, and then re-cluster the underlying tables based on the most frequently used join keys.
E. Increase the virtual warehouse size to provide more resources for query processing. Convert the view into a table using CREATE TABLE AS SELECT (CTAS).

Answer: A,B,D

Explanation:
Materialized views (A) can significantly improve performance by precomputing the results. JOIN hints (B) help the query optimizer choose the most efficient join strategy. Analyzing the query profile and re-clustering (C) addresses potential data skew and inefficient join processing. Increasing warehouse size (D) can help, but it's not a targeted solution for join performance. Partitioning (E) isn't directly supported in Snowflake; clustering is the analogous approach.

NEW QUESTION # 224
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