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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q57-Q62):

NEW QUESTION # 57
You are implementing row access policies on a 'SALES DATA table to restrict access based on the 'REGION' column. Different users are allowed to see data only for specific regions. You have a mapping table 'USER REGION MAP' with columns 'USERNAME' and 'REGION'. You want to create a row access policy that dynamically filters the 'SALES DATA' based on the user and their allowed region. Which of the following options represents a correct approach to create and apply this row access policy?

A. Option D
B. Option E
C. Option B
D. Option A
E. Option C

Answer: C

Explanation:
Option B is the correct approach. It creates a row access policy that checks if a row exists in the where the username matches the current user and the region matches the 'REGION' column in the 'SALES_DATR table. 'ADD ROW ACCESS POLICY is the correct command to apply the policy. Options A is incorrect as it uses IN clause, which can become inefficient with large datasets. Option C uses 'SET' which is not a valid operation, and Option D uses 'MODIFY which is used for masking policy and not row access policy. Option E uses 'CURRENT ROLE instead of 'CURRENT USER' which is not the appropriate filter criteria.

NEW QUESTION # 58
You are tasked with building a data pipeline that ingests customer interaction data from multiple microservices using Snowpipe Streaming. Each microservice writes data in JSON format to its own Kafka topic. You need to design an efficient and scalable solution to ingest this data into a single Snowflake table, while ensuring data integrity and minimizing latency. Consider these constraints: 1. High data volume with variable ingestion rates. 2. The need to correlate data from different microservices based on a common 'customer id'. 3. Potential for schema evolution in the microservices. Given these requirements and constraints, which of the following architectural approaches, leveraging Snowpipe Streaming features and Snowflake capabilities, would be the MOST appropriate and robust?

A. Use a single Snowpipe Streaming client to ingest data from all Kafka topics into a single VARIANT column in the Snowflake table. Then, use Snowflake's external functions to transform and load the data into the final target table based on the 'customer_id'
B. Create a separate Snowpipe Streaming client for each Kafka topic, ingesting data into separate staging tables. Then, use a scheduled task to merge the data into the final target table based on 'customer id'.
C. Develop a single Snowpipe Streaming client that consumes data from all Kafka topics, using a transformation function to route the data to the correct table based on the topic name. Use Snowflake's clustering key on 'customer _ id' for efficient querying.
D. Implement a custom Kafka Connect connector that directly writes data to Snowflake using Snowpipe Streaming. The connector should handle schema evolution and routing based on topic name. Define a clustering key on the Snowflake table on the 'customer id'
E. Develop a Spark Streaming application that reads data from Kafka, transforms it, and then uses the Snowflake Connector for Spark to write the data to Snowflake in micro-batches.

Answer: D

Explanation:
D is the MOST appropriate solution. A custom Kafka Connect connector provides the most robust and scalable approach. It can handle consuming from multiple topics, manage schema evolution, and use Snowpipe Streaming directly for ingestion. A is less efficient due to multiple Snowpipe clients and scheduled merging. B is difficult to maintain with schema evolution and routing using transformation functions. C is inefficient due to post-ingestion transformation. E, while viable, introduces the complexity and overhead of Spark when Snowpipe Streaming offers a more direct solution.

NEW QUESTION # 59

A. The Lambda function is returning a string instead of a number. Modify the Lambda function to return the discount as a number (e.g., 'discount = 0.15' instead of 'discount = '0.15")
B. The Lambda function returns the discount within a nested JSON structure Tdata': [[discount]]}'. The Snowflake function is not designed to handle this. The lambda function should return '{'data':
C. The Snowflake external function is not correctly parsing the JSON response from the Lambda function. Implement a wrapper function in Snowflake to parse the JSON and extract the discount value before returning it.
D. The 'RETURNS NULL ON NULL INPUT clause in the external function definition is causing the function to return NULL even when valid inputs are provided. Remove this clause.
E. The data types in the Lambda function and Snowflake function definition do not match. Specifically, the Lambda function expects strings while Snowflake is sending numbers and vice versa. Modify the Lambda function to handle numeric inputs and ensure the Snowflake function definition aligns with the expected output data type (FLOAT).

Answer: C

Explanation:
The most likely cause is (B). Snowflake expects the external function to return a single value directly convertible to the declared return type. The Lambda function is returning a JSON object that needs to be parsed. Snowflake needs a wrapper function to extract the numerical result from the json response. All other issues have been taken care of in the question and is not the cause of the problem.

NEW QUESTION # 60
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 the masking rules in a separate Snowflake table and querying them within the stored procedure.
B. Storing masking logic in Javascript UDFs and calling these UDFs dynamically within the stored procedure based on column names and datatype
C. Using external stages and pulling the masking rules from a configuration file during stored procedure execution.
D. Hardcoding the masking rules directly within the JavaScript stored procedure.
E. Defining the masking rules as JSON objects within the stored procedure code.

Answer: A,B

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 # 61
Your team is developing a set of complex analytical queries in Snowflake that involve multiple joins, window functions, and aggregations on a large table called 'TRANSACTIONS. These queries are used to generate daily reports. The query execution times are unacceptably high, and you need to optimize them using caching techniques. You have identified that the intermediate results of certain subqueries are repeatedly used across different reports, but they are not explicitly cached. Given the following options, which combination of strategies would MOST effectively utilize Snowflake's caching capabilities to optimize these analytical queries and improve report generation time?

A. Utilize the "RESULT_SCAN' function in conjunction with the query ID of the initial subquery execution to explicitly cache and reuse the results in subsequent queries. This approach requires careful management of query IDs.
B. Create materialized views that pre-compute the intermediate results of the subqueries. This will allow Snowflake to automatically refresh the materialized views when the underlying data changes and serve the results directly from the cache.
C. Create common table expressions (CTEs) for the subqueries and reference them in the main query. CTEs will force Snowflake to cache the results of the subqueries, improving performance.
D. Use temporary tables to store the intermediate results of the subqueries. These tables will be automatically cached by Snowflake and can be reused by subsequent queries within the same session.
E. Consider using 'CACHE RESULT for particularly expensive subqueries or views. This is a hint to snowflake to prioritize caching the result set for future calls.

Answer: B,E

Explanation:
Creating materialized views (D) for the intermediate results is the most effective approach, as Snowflake automatically manages the refresh and caching. 'CACHE RESULT (E) Provides a way to explicitly cache the results. Temporary tables (A) are session-specific and not suitable for persistent caching across reports. CTEs (B) do not guarantee caching and are primarily for query readability. 'RESULT SCAN' (C) is complex to manage and requires manual tracking of query IDs. Therefore, a combination of materialized views and CACHE RESULT will provide the best caching strategy.

NEW QUESTION # 62
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