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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) Sample Questions (Q32-Q37):

NEW QUESTION # 32
You are tasked with building a data pipeline that ingests JSON data from a series of publically accessible URLs. These URLs are provided as a list within a Snowflake table 'metadata_table', containing columns 'file_name' and 'file url'. Each JSON file contains information about products. You need to create a view that extracts product name, price, and a flag indicating whether the product description contains the word 'discount'. Which of the following approaches correctly implements this, optimizing for both performance and minimal code duplication, using external functions for text processing?

A. Create an external function that takes a string as input and returns a BOOLEAN whether that string contains 'discount. Create a view on top of metadata_table', and using 'SYSTEM$URL_GET' fetch the content from 'file_url'. The JSON can then be parsed and the fields like price, name and description can be fetched. Use within the view to flag the presence of discount.
B. Create an external function that takes a URL as input and returns a JSON variant containing the extracted product name, price, and discount flag (using 'LIKE Then, create a view that selects from calls the external function with 'SYSTEM$URL as input, and extracts the desired attributes from the returned JSON variant. A stage must also be created to host external function code.
C. Create an external function that takes a URL as input and returns a BOOLEAN indicating if any error occured while processing the URL and the data. Create a stored procedure that iterates through 'metadata_table' , calls external function for each URL, reports error and then processes the data. A stage must also be created to host external function code.
D. Create a stored procedure that iterates through 'metadata_table', downloads each JSON file using 'SYSTEM$URL GET, parses the JSON, extracts the required fields, and inserts the data into a target table. Then, create a view on top of the target table. Use 'LIKE '%discount%' to identify if a product description contains the word 'discount'.
E. Create a pipe using 'COPY INTO' statement with 'FILE FORMAT = (TYPE = JSON)' and 'ON_ERROR = CONTINUE that loads the JSON files directly into a staging table. Create a view on top of the staging table to extract the required fields. The must have = TRUE' configured if JSON files are nested array. Use ' ILIKE in your view for the discount flag.

Answer: A,B

Explanation:
Option B correctly leverages an external function to encapsulate the logic of fetching and processing the JSON data from the URL. The external function promotes code reusability and reduces complexity in the view. Option E is also correct as it demonstrates how to process fetched JSON data and use a UDF to enhance the transformation. Option A involves a procedural approach that is less efficient than using an external function or pipes. Option C does not directly work with data URLs and is geared more towards data residing within Snowflake storage. Option D is incorrect because it creates the external function just for identifying errors and creates a stored procedure just to process the data.

NEW QUESTION # 33
You are tasked with designing a data sharing solution where data from multiple tables residing in different databases within the same Snowflake account needs to be combined into a single view that is then shared with a consumer account. The view must also implement row-level security based on the consumer's role. Which of the following options represent valid approaches for implementing this solution? Select all that apply.

A. Create a secure view that joins tables from different databases and implement row-level security using a row access policy based on the CURRENT ROLE() function. Masking policy cannot provide role based access control so will not work.
B. Create a standard view with a stored procedure to handle the joins across databases and use EXECUTE AS OWNER to avoid permission issues. This standard view should be shared.
C. Create a view for each table and then build a final view using 'UNION ALL' to combine data from all the views and implement row-level security with a role based row access policy. Standard views should not be used in data sharing.
D. Create a standard view that joins tables from different databases using aliases and implement row-level security using a UDF that checks the consumer's role and filters the data accordingly.
E. Create a secure view that joins tables from different databases using fully qualified names (e.g., 'DATABASEI .SCHEMAI . TABLET) and implement row-level security using a masking policy based on the CURRENT_ROLE() function.

Answer: A,E

Explanation:
Options A and C are the valid approaches. A secure view is essential for data sharing. Fully qualified names are required to reference objects across databases. Row-level security can be implemented using either a row access policy or a masking policy (with some limitations). Option A incorrectly mentions a masking policy for row level access control. Option C is correct as row access policies are designed for that case. Option D create standard views and sharing standard views is not a good practice. Stored procedures cannot be used in the definition of a view for data sharing, so E is invalid. B is also invalid as standard view.

NEW QUESTION # 34
Consider a table 'EVENT DATA' that stores events from various applications. The table has columns like 'EVENT ID, 'EVENT TIMESTAMP, 'APPLICATION ID', 'USER ID', and 'EVENT _ TYPE. A significant portion of queries filter on 'EVENT TIMESTAMP ranges AND 'APPLICATION ID. The data volume is substantial, and query performance is crucial. You observe high clustering depth after initial loading. Which combination of actions will provide the MOST effective performance optimization, addressing both clustering depth and query performance?

A. Create separate tables for each ' , each clustered on 'EVENT_TIMESTAMP'. Then, create a view that UNION ALLs these tables.
B. Cluster the table on 'EVENT TIMESTAMP' and periodically run 'OPTIMIZE TABLE EVENT DATA' using a small warehouse. Also, create a separate table clustered on 'APPLICATION
C. Cluster the table on '(EVENT TIMESTAMP, APPLICATION IDY and periodically run 'OPTIMIZE TABLE EVENT DATA' using a warehouse sized appropriately for the table size. Then, monitor clustering depth regularly.
D. Create multiple materialized views: one filtering on common 'EVENT TIMESTAMP' ranges, and another filtering on common 'APPLICATION ID' values.
E. Cluster the table on 'USER ICY and rely solely on Snowflake's automatic reclustering feature, without running 'OPTIMIZE TABLES manually.

Answer: C

Explanation:
Clustering on '(EVENT _ TIMESTAMP, APPLICATION_ID)' directly addresses the common query patterns. Regularly running 'OPTIMIZE TABLE EVENT DATA' with an appropriately sized warehouse ensures the data remains well-clustered as new data is added, reducing clustering depth and maintaining performance. Monitoring clustering depth is essential to identify when reclustering is needed. Clustering on a single dimension like 'USER IDS (C) doesn't address the primary query patterns. Creating separate tables (A, D) introduces complexity and management overhead. Materialized views (E) are helpful for specific pre-aggregated results, but clustering optimizes the base table for a wider range of queries. Optimizing with the right sized warehouse is crucial, a small warehouse might take an extremely long time.

NEW QUESTION # 35
You need to define a UDF in Snowflake that takes a date as input and returns the next business day (Monday-Friday). If the input date is a Friday, the UDF should return the following Monday. If the input date is a Saturday or Sunday, the function should return the following Monday as well. Which of the following UDF definitions correctly implements this logic?

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

Answer: C

Explanation:
Option E correctly handles all the scenarios. It first checks if the day is a Friday (DAYOFWEEK = 5) and adds 3 days. Then, it checks if the day is a Saturday or Sunday (DAYOFWEEK IN (6, 7)) and calculates the correct number of days to add to reach Monday (8 - DAYOFWEEK(input_date)). If it's any other day, it simply returns the input date as the question only asks to move to the next business day and not the next date if its already a business day. Option A is wrong because it always adds 1 day to Monday-Thursday which does not meet the requirement, the same goes to option B as well. Option C is wrong because Monday - Thursday will return the same date. Option D doesn't return next business day, but the next day.

NEW QUESTION # 36
Given the following scenario: You have an external table 'EXT SALES in Snowflake pointing to a data lake in Azure Blob Storage. The storage account network rules are configured to only allow specific IP addresses and virtual network subnets, enhancing security. You are getting intermittent errors when querying 'EXT SALES. Which of the following could be the cause(s) and the corresponding solution(s)? Select all that apply.

A. The Snowflake service principal does not have the correct permissions on the Azure Blob Storage account. Solution: Ensure the Snowflake service principal has the 'Storage Blob Data Reader' role assigned to it.
B. The table function cache is stale, causing access to non-existent files. Solution: Run 'ALTER EXTERNAL TABLE EXT_SALES REFRESH'.
C. The network connectivity between Snowflake and Azure Blob Storage is unstable. Solution: Implement retry logic in your queries to handle transient network errors.
D. The Snowflake IP addresses used to access the Azure Blob Storage are not whitelisted in the storage account's firewall settings. Solution: Obtain the Snowflake IP address ranges for your region and add them to the storage account's allowed IP addresses.
E. The file format specified in the external table definition does not match the actual format of the files in Azure Blob Storage. Solution: Update the 'FILE_FORMAT parameter in the external table definition to match the correct file format.

Answer: A,D

Explanation:
Options A and C are the most likely causes. Network restrictions often lead to connectivity issues if Snowflake's IP addresses are not whitelisted (A). Incorrect permissions for the Snowflake service principal (C) will also prevent access to the data lake. Option B is relevant if there are issues around schema changes or new files added. Option D will cause errors all the time not intermittently. Option E Snowflake automatically retries some queries. In a secure environment where IP whitelisting is mandated and IAM roles are properly configured, intermittent failures suggest a permission or a networking issue.

NEW QUESTION # 37
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