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A company is using Snowflake's web app interface to manage its data'. A data engineer needs to create a new table, load data into it from a CSV file stored in an internal stage, and then grant SELECT privileges on the table to a specific role using the web app. Which sequence of actions within the Snowflake web app represents the most efficient and secure way to accomplish this task?
A. 1. Use the Database Tables interface to create the new table using the table editor. 2. Use the Data Load Data wizard to load the CSV file. 3. Use the SQL worksheet to execute GRANT SELECT ON TABLE statement.
B. 1. Use the SQL worksheet to execute CREATE TABLE statement. 2. Use the Database -> Tables interface, select the table, and use the 'Load Data' option to load the CSV file. 3. Use the Database -> Tables interface, select the table, and use the 'Privileges' tab to grant SELECT privilege to the role.
C. 1. Use the SQL worksheet to execute CREATE TABLE statement. 2. Use the Data Load Data wizard to load the CSV file. 3. Use the SQL worksheet to execute GRANT SELECT ON TABLE statement.
D. 1. Use the Database Tables interface to create the new table using the table editor. 2. Use the Data Load Data wizard to load the CSV file. 3. Use the Database -> Tables interface, select the table, and use the 'Privileges' tab to grant SELECT privilege to the role.
E. 1. Use the Database Tables interface to create the new table using the table editor. 2. Upload the CSV file directly to the table using the 'Load Data' option. 3. Use the SQL worksheet to execute GRANT SELECT ON TABLE statement.
Answer: A
Explanation:
Option E is the most efficient and secure option, as it leverages both the graphical interface and SQL for specific tasks. Using the Table interface in option A is clunky. Option B is inefficient as Data -> Load Data Wizard is preferrable to uploading via the table interface itself. Option C uses the SQL Worksheet and lacks security. Option D is redundant. Option E provides a good balance. Creating the table via the GUI, loading the data via the load data wizard and then granting privileges by the SQL Worksheet is most efficent.
NEW QUESTION # 260
You are tasked with sharing a subset of a customer table (CUSTOMER DATA') residing in your organization's Snowflake account with a partner organization. You need to mask personally identifiable information (PII) while providing near real-time updates. You decide to use a secure view. Which of the following SQL statements is the MOST efficient and secure way to accomplish this, assuming the partner only needs 'customer id', 'masked_email', 'city', and 'state'? The email should be masked using SHA256.
A. Option E
B. Option D
C. Option B
D. Option A
E. Option C
Answer: D
Explanation:
Option A correctly creates a SECURE VIEW, which is essential for data sharing as it prevents the partner from seeing the underlying table definition. It directly masks the email using SHA256. While Option C uses a hypothetical 'SYSTEM$MASK EMAIL' function, SHA256 is a readily available and standard masking method. Options B, D, and E do not create a secure view or properly grant access for data sharing. The correct method to share is to create secure view and then share using the share object.
NEW QUESTION # 261
A data warehousing team is experiencing inconsistent query performance on a large fact table C SALES FACT) that is updated daily. Some queries involving complex joins and aggregations take significantly longer to execute than others, even when run with the same virtual warehouse size. You suspect that the query result cache is not being effectively utilized due to variations in query syntax and the dynamic nature of the data'. Which of the following strategies could you implement to maximize the effectiveness of the query result cache and improve query performance consistency? Assume virtual warehouse size is large and the data is skewed across days.
A. Optimize the 'SALES_FACT table by clustering it on the most frequently used filter columns and enabling automatic clustering. This will improve data locality and reduce the amount of data that needs to be scanned.
B. Use stored procedures with parameters to encapsulate the queries. This will ensure that the query syntax is consistent, regardless of the specific parameters used.
C. Create a separate virtual warehouse specifically for running these queries. This will isolate the cache and prevent it from being invalidated by other queries.
D. Implement query tagging to standardize query syntax. By applying consistent tags to queries, you can ensure that similar queries are recognized as identical and reuse cached results.
E. Implement a data masking policy on the 'SALES_FACT table. Data masking will reduce the size of the data that needs to be cached, improving cache utilization.
Answer: A,B
Explanation:
Using stored procedures with parameters (B) standardizes query syntax, making it easier for Snowflake to recognize and reuse cached results. Optimizing the 'SALES_FACT table with clustering (E) improves data locality, reducing the amount of data that needs to be scanned and potentially cached. Query tagging (A) can help, but might not address subtle syntax differences. Creating a separate virtual warehouse (C) doesn't guarantee better cache utilization; it just isolates it. Data masking (D) is primarily for security, not caching, although it might indirectly reduce data size.
NEW QUESTION # 262
Consider a scenario where you're optimizing a data pipeline in Snowflake responsible for aggregating sales data from multiple regions. You've identified that the frequent full refreshes of the target aggregated table are causing significant performance overhead and resource consumption. Which strategies could be employed to optimize these full refreshes without sacrificing data accuracy?
A. Implement incremental data loading using streams and tasks. This allows you to only process and load the changes that have occurred since the last refresh, reducing the amount of data that needs to be processed.
B. Utilize Snowflake's Time Travel feature to clone the previous version of the aggregated table, apply the necessary changes to the clone, and then swap the clone with the original table using 'ALTER TABLE SWAP WITH'. Note that this will impact data availability during the swap operation.
C. Replace the full refresh with a 'TRUNCATE TABLE' followed by an 'INSERT statement. This approach is faster than 'CREATE OR REPLACE TABLE' and reduces locking.
D. Leverage Snowflake's search optimization service on the base tables. While costly, this will dramatically speed up full table scans performed in the aggregation.
E. Schedule the full refreshes during off-peak hours when the Snowflake warehouse is less utilized. This minimizes the impact on other workloads but does not reduce the actual processing time.
Answer: A,B
Explanation:
Options A and C are the most effective strategies. Incremental data loading (Option A) focuses on processing only the changed data, significantly reducing the processing time and resources used. Cloning and swapping (Option C) can provide a faster refresh while maintaining data availability (with a brief interruption during the swap). Option B, while faster than 'CREATE OR REPLACE TABLE, is still a full refresh and inefficient. Option D only mitigates the impact, not the underlying inefficiency. Option E will help improve performance but can be costly, should only be implemented for specific columns/tables and does not reduce the need for optimizing the data pipeline's refresh strategy directly.
NEW QUESTION # 263
You are using Snowpipe to ingest data from Azure Blob Storage into a Snowflake table. You have successfully set up the pipe and configured the event notifications. However, you notice that duplicate records are appearing in your target table. After reviewing the logs, you determine that the same file is being processed multiple times by Snowpipe. Which of the following strategies can you implement to prevent duplicate data ingestion, assuming you cannot modify the source data in Azure Blob Storage to include a unique ID or timestamp?
A. Modify the Azure Event Grid subscription configuration to filter events based on file size or creation time to avoid resending events for already processed files.
B. Create a Snowflake stream on the target table and use it to incrementally load data into a separate, deduplicated table using a merge statement with conditional logic to insert or update records based on a combination of columns.
C. Configure the Snowpipe definition with the 'PURGE = TRUE parameter. This will ensure that each file is only processed once.
D. Use a data masking policy with the 'MASK' function to obfuscate duplicate records based on their similarity, making them effectively invisible to downstream queries.
E. Implement idempotent logic within a Snowflake stored procedure that is triggered by a task after the data is loaded by Snowpipe. The stored procedure should identify and remove duplicate rows based on all other columns in the table.
Answer: B
Explanation:
Using a Snowflake stream (C) is the most effective and scalable solution for handling duplicate data ingestion in this scenario. Streams provide change data capture (CDC) capabilities, allowing you to track changes to the target table caused by Snowpipe. By creating a stream and then using a merge statement with a combination of columns, you can identify and handle duplicates effectively during the incremental load into a separate table. Option A, while functional, would require processing the entire table after each Snowpipe load, which is not efficient for large datasets. Option B, PURGE = TRUE, is not valid for Snowpipe. Data masking does not help prevent or remove duplicate records. While Option E sounds promising, it doesn't provide guarantee of removing duplicates.
NEW QUESTION # 264
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