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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q315-Q320):

NEW QUESTION # 315
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. Defining the masking rules as JSON objects within the stored procedure code.
C. Storing masking logic in Javascript UDFs and calling these UDFs dynamically within the stored procedure based on column names and datatype
D. Storing the masking rules in a separate Snowflake table and querying them within the stored procedure.
E. Hardcoding the masking rules directly within the JavaScript stored procedure.

Answer: C,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 # 316
A Snowflake table, contains product information in a VARIANT column named This column holds JSON structures. You need to create a view, , that exposes specific fields , and 'category') as structured columns, and should gracefully handle scenarios where may contain characters incompatible with VARCHAR, 'category' is nested inside an array called 'tags'. What is the BEST and the MOST robust approach?

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

Answer: E

Explanation:
Option E is the best because it uses 'TRY CAST to safely convert 'product_name' to VARCHAR, handling potential invalid characters without causing the query to fail. Further, to select one category element from array of 'tags' , the way it is done in other options is wrong and we should use 'FLATTEN' with limit 1. Option A will cause problems if 'product_name' cannot be directly cast to VARCHAR. Option B is flawed as 'GET_PATH' requires specific index and its unnecessarily complicated for a simple array access, and if the tags array has other elements, this will arbitrarily get the first tag instead of consistently getting the intended first element (if exists). Option C and D don't use 'LIMIT 1 ' and so throw error as subquery returns more than 1 row.

NEW QUESTION # 317
You are tasked with implementing a data recovery strategy for a critical table 'SALES DATA' in Snowflake. The table is frequently updated, and you need to ensure you can recover to a specific point in time in case of accidental data corruption. Which approach provides the most efficient and granular recovery option, minimizing downtime and data loss? Consider performance and storage implications of each method.

A. Regularly creating full clones of the 'SALES_DATR table to a separate database.
B. Using the 'UNDROP TABLE command in conjunction with the 'AT' clause to revert the table to a previous state.
C. Creating a Snowflake Stream on 'SALES_DATR and capturing all DML changes for point-in-time recovery.
D. Create a scheduled task that takes a snapshot of the sales data and store it to an external staging location.
E. Relying solely on Snowflake's Time Travel feature with the default data retention period.

Answer: C

Explanation:
Option C is correct: Streams provide a granular way to capture DML changes. While Time Travel provides recovery, a stream specifically captures the changes, allowing for more controlled replay and point-in-time recovery. Full clones are resource-intensive, "UNDROP" is for dropped tables, not general recovery, and relying solely on default Time Travel may not meet specific recovery time objectives.

NEW QUESTION # 318
You are tasked with creating a Python script to load data from a CSV file stored in an AWS S3 bucket into a Snowflake table. You have the following requirements: 1. Use the 'COPY INTO' command for efficient data loading. 2. Handle potential schema evolution in the CSV file (e.g., new columns being added). 3. Automatically create the target table if it doesn't exist, inferring the schema from the CSV. Which combination of Snowflake Python connector functionalities and 'COPY INTO' options would best address these requirements, assuming you have an AWS IAM role configured for Snowflake access to S3?

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

Answer: A

Explanation:
Option C is the most effective solution. 'ON_ERROR = CONTINUE allows the 'COPY INTO' command to proceed even if it encounters errors (e.g., due to extra columns). = CASE INSENSITIVE' handles slight variations in column names between the CSV and the Snowflake table. Setting = TRUE in the Snowflake session (this is a hypothetical feature, Snowflake doesn't directly have AUTO_CREATE_TABLE in COPY INTO, so this tests knowledge of workarounds and understanding of COPY INTO capabilities). Other options have drawbacks. Option A doesn't directly use COPY INTO and is generally slower. Option B requires a predefined schema, negating the ability to handle schema evolution. Option D is used with in memory stream, rather s3. Option E involves manual schema inspection and construction, which is cumbersome and error-prone.

NEW QUESTION # 319
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. Create a materialized view that aggregates data by 'CUSTOMER_ID and relevant dimensions.
B. Cluster the 'SALES DATA' table on 'CUSTOMER ID.
C. Create a secondary index on 'CUSTOMER ID'
D. Partition the table by 'CUSTOMER_ID instead of 'SALE_DATE.
E. Increase the virtual warehouse size.

Answer: B

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 # 320
......

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