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DEA-C02 Exam Dump, Official DEA-C02 Practice Test

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q337-Q342):

NEW QUESTION # 337
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 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.
B. 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.
C. 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.
D. 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.
E. 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.

Answer: A,C,D,E

Explanation:
সব options except D are likely causes of performance degradation. A poorly indexed 'user_groups' table (A) will slow down policy evaluation. Complex policy logic (B) can also impact performance. Interference with data pruning (C) is a common issue with row access policies. Full table scans (E) can be exacerbated by the policy if data is not clustered appropriately. Users needing explicit privileges to 'user_groups' is not needed since the policy handles that; also using a secure view handles that as well.

NEW QUESTION # 338
You have a Python UDF in Snowflake designed to enrich customer data by calling an external API to retrieve additional information based on the customer ID. Due to API rate limits, you need to implement a mechanism to cache API responses within the UDF to avoid exceeding the limits. The UDF is defined as follows:

Which caching mechanism can be implemented MOST effectively WITHIN the Python UDF to minimize API calls while adhering to Snowflake's UDF limitations?

A. Utilize Snowflake's built-in caching mechanisms (result caching) by ensuring the UDF is deterministic and only depends on its input parameters. Snowflake will automatically cache the results of the UDF for subsequent calls with the same input.
B. Leverage external caching services like Redis by making API calls to Redis from the UDF to store and retrieve cached API responses. This would require configuring Snowflake to connect with external systems.
C. Persist the API responses in a temporary table within Snowflake. The UDF will first query the temporary table for the customer ID; if found, return the cached data. Otherwise, call the API and store the response in the temporary table for future use.
D. Create a global dictionary within the UDF to store the API responses, using the customer ID as the key. Before calling the API, check if the customer ID exists in the dictionary; if it does, return the cached response. This approach will keep cached values during the session.
E. Use the 'functools.lru_cache' decorator to cache the results of the 'get_customer details' function within the UDF's scope. This will automatically cache the most recently used API responses.

Answer: E

Explanation:
Using 'functools.lru_cache' (Option A) is the most efficient and straightforward solution. It provides a built-in caching mechanism within the Python UDF's scope without requiring external dependencies or complex manual caching logic. Option B is not the best, as it will cause issues in multithreaded environment where this is not thread safe and could cause data inconsistency. Option C is related to Snowflake result cache which is independent of UDF cache needs and concerns. The temp table (option D) adds overhead by querying external tables within the UDF, making API execution slower rather than faster. And Option E needs external connections which increase infrastructure complexity.

NEW QUESTION # 339
You're designing a data masking solution for a 'CUSTOMER' table with columns like 'CUSTOMER ID', 'NAME', 'EMAIL', and 'PHONE NUMBER. You want to implement the following requirements: 1. The 'SUPPORT' role should be able to see the last four digits of the 'PHONE NUMBER and a hashed version of the 'EMAIL'. 2. The 'MARKETING' role should be able to see the full 'NAME' and a domain-only version of the 'EMAIL' (everything after the '@' symbol). 3. সব other roles should see masked values for 'EMAIL' and 'PHONE NUMBER. Which of the following masking policy definitions BEST achieves these requirements using Snowflake's built-in functions and RBAC?

Answer: A

Explanation:
Option D is the best solution as it effectively uses 'SHA2 for the hashed email for support, 'SUBSTRING(email, POSITION('@' IN email) + 1)' correctly extracts the domain, and LENGTH(phone)-4), RIGHT(phone, 4))' creates the 'XXXXXXXXXX' and then the final four digits of the phone. Other options are not correct because they may use incorrect functions, or because they use outdated syntax (CONCAT instead of 'IF). The correct solution uses the correct functions, SHA2()' for ইমেইল Hash for support User, 'SUBSTRING(email, POSITION('@' IN email) + 1)' extract Domain name of the ইমেইল for Marketing User, LENGTH(phone)-4), RIGHT(phone, 4))' masking the Phone number by preserving last four digits for Support User.

NEW QUESTION # 340
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. Using external stages and pulling the masking rules from a configuration file during stored procedure execution.
B. Hardcoding the masking rules directly within the JavaScript stored procedure.
C. Storing the masking rules in a separate Snowflake table and querying them within the stored procedure.
D. Defining the masking rules as JSON objects within the stored procedure code.
E. Storing masking logic in Javascript UDFs and calling these UDFs dynamically within the stored procedure based on column names and datatype

Answer: C,E

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 # 341
A data engineer is responsible for maintaining a Snowflake data warehouse. They notice a significant slowdown in the performance of a specific query that aggregates data from a table called 'SALES DATA', which contains billions of rows. The query is used for generating daily sales reports. The engineer suspects that the issue might be related to clustering. How would you diagnose the effectiveness of the clustering on the 'SALES DATA' table and identify potential improvements?

A. Use the SYSTEM$CLUSTERING_INFORMATION' function to analyze the clustering depth of the table. A high clustering depth indicates poor clustering.
B. Use the 'DESCRIBE TABLE SALES_DATA' command and check the 'clustering_key' property, then run 'SELECT SYSTEM$MEASURE CLUSTERING DEPTH('SALES to check the average depth of the table. Compare the clustering depth to the number of micro- partitions to assess clustering effectiveness. A depth closer to zero is best.
C. Use the 'VALIDATE table command. This command detects fragmentation in the data due to poor clustering.
D. Examine the query profile in the Snowflake web interface to identify stages that are scanning large amounts of data. Check if these stages are benefiting from clustering.
E. Use the 'SHOW TABLES command to view the clustering key defined on the table. Verify that the clustering key is appropriate for the query workload.

Answer: A

Explanation:
Option A provides the most direct way to assess clustering effectiveness. 'SYSTEM$CLUSTERING INFORMATION' provides detailed metrics, including clustering depth, which directly indicates how well the data is clustered based on the clustering key. A high clustering depth suggests poor clustering, meaning that many micro-partitions need to be scanned to satisfy a query. Option B is helpful to confirm the key, but doesn't diagnose effectiveness. Option C is useful for identifying large scans but doesn't isolate clustering issues. Option D isn't a valid Snowflake command, there's no ' VALIDATE' command. Option E is very nearly correct, but it combines two separate operations, and 'SYSTEM$MEASURE_CLUSTERING DEPTH' requires the table name in the format 'database_name.schema_name.table_name'.

NEW QUESTION # 342
......

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Chris Howard Chris Howard

আমার সম্পর্কে

DEA-C02 Exam Dump, Official DEA-C02 Practice Test

Preparation Material with ফ্রি Demos and Updates [2025]

Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q337-Q342):

ফ্রি PDF Quiz Snowflake - Newest DEA-C02 Exam Dump

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