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A Snowflake data engineer is troubleshooting a performance issue with a query that retrieves data from a large table (TRANSACTIONS). The table has a VARIANT column containing semi-structured JSON data representing transaction details. The query uses several LATERAL FLATTEN functions to extract specific fields from the JSON and filters the data based on these extracted values. Despite having adequate virtual warehouse resources, the query is running slower than expected. Identify the MOST effective strategy to improve the performance of this query:
A. Convert the VARIANT column to a VARCHAR column and store the JSON data as a string.
B. Rewrite the query to use regular expressions instead of LATERAL FLATTEN for extracting the fields from the JSON data.
C. Increase the virtual warehouse size to provide more memory for processing the JSON data.
D. Create a new table with pre-extracted fields from the VARIANT column and use this table in the query instead of the LATERAL FLATTEN operations.
E. Create a search optimization service on the TRANSACTIONS table for the VARIANT column.
Answer: D
Explanation:
Pre-extracting the required fields from the VARIANT column into separate columns in a new table significantly improves query performance by eliminating the need for expensive LATERAL FLATTEN operations at query time. Option A might help slightly, but pre-extraction is more impactful. Option C is unlikely to be faster. Option D, while applicable to VARIANT columns, is better suited for point lookups, not large-scale extraction and filtering. Option E would negate the benefits of the VARIANT data type.
NEW QUESTION # 200
You have an external table in Snowflake pointing to data in Azure Blob Storage. The data consists of customer transactions, and new files are added to the Blob Storage daily You want to ensure that Snowflake automatically picks up these new files and reflects them in the external table without manual intervention. However, you are observing delays in Snowflake detecting the new files. What are the potential reasons for this delay and how can you troubleshoot them? (Choose two)
A. The storage integration associated with the external table does not have sufficient permissions to access the Blob Storage.
B. The Azure Event Grid notification integration is not properly configured to notify Snowflake about new file arrivals in the Blob Storage.
C. Snowflake's internal cache is not properly configured; increasing the cache size will solve the problem.
D. The file format used for the external table is incompatible with the data files in Blob Storage.
E. The external table's 'AUTO_REFRESH' parameter is set to 'FALSE', which disables automatic metadata refresh.
Answer: B,E
Explanation:
The two primary reasons for delays in Snowflake detecting new files in an external table are: 1) Incorrect configuration of the cloud provider's notification service (Azure Event Grid in this case). Snowflake relies on these notifications to be informed about new file arrivals. If the integration isn't set up correctly, Snowflake won't know when to refresh the metadata. 2) The parameter must be set to ' TRUE' for automatic metadata refresh to occur. If it's set to FALSE , manual refreshes are required using 'ALTER EXTERNAL TABLE ... REFRESH". Options D and E, although possible issues, won't directly cause a delay in detecting new files, but rather cause issues accessing files after detection. Option C is irrelevant as Snowflake's caching mechanism does not directly impact external table metadata refresh.
NEW QUESTION # 201
You are designing a data pipeline to load JSON data from an AWS S3 bucket into a Snowflake table. The JSON files have varying schemas, and you want to use schema evolution to handle changes. You are using a named external stage with 'AUTO REFRESH = TRUE. You notice that some files are not being ingested, and the COPY HISTORY shows 'Invalid JSON' errors. Which of the following actions would BEST address this issue while minimizing manual intervention?
A. Create a separate landing stage for potentially invalid JSON files and use a task to validate the files before moving them to the main stage for ingestion into Snowflake.
B. Modify the COPY INTO statement to include 'ON ERROR = SKIP FILE' to ignore files with invalid JSON and continue loading other files. This ensures the pipeline continues without interruption.
C. Implement a pre-processing step using a Snowpark Python UDF to cleanse the JSON files in the stage before the COPY INTO command is executed. This UDF should handle schema variations and correct any invalid JSON structures.
D. Adjust the file format definition associated with the stage to be more permissive, allowing for variations in the JSON structure. For example, use 'STRIP OUTER ARRAY = TRUE and configure error handling within the file format.
E. Re-create the stage with the 'AUTO REFRESH = FALSE parameter and manually refresh the stage metadata after each file is uploaded. This gives more control over which files are processed.
Answer: C
Explanation:
The best approach is to use a Snowpark Python UDF (option C) to pre-process and cleanse the JSON files. This allows for handling schema variations and correcting invalid JSON structures before loading them into Snowflake. 'ON ERROR = SKIP FILE might skip important data without proper investigation. A landing stage (option B) adds complexity and requires additional automation. Making the file format too permissive (option D) may lead to incorrect data loading. Disabling auto-refresh (option E) defeats the purpose of a continuous data pipeline.
NEW QUESTION # 202
You have created an external table in Snowflake that points to a large dataset stored in Azure Blob Storage. The data consists of JSON files, and you've noticed that query performance is slow. Analyzing the query profile, you see that Snowflake is scanning a large number of unnecessary files. Which of the following strategies could you implement to significantly improve query performance against this external table?
A. Create a materialized view on top of the external table to pre-aggregate the data.
B. Increase the size of the Snowflake virtual warehouse to provide more processing power.
C. Create an internal stage, copy all JSON Files, create and load the target table, and drop external table
D. Convert the JSON files to Parquet format and recreate the external table to point to the Parquet files.
E. Partition the data in Azure Blob Storage based on a relevant column (e.g., date) and define partitioning metadata in the external table definition using PARTITION BY.
Answer: D,E
Explanation:
Partitioning the data (B) allows Snowflake to prune unnecessary files during query execution, significantly improving performance. Converting to Parquet (C) provides a columnar storage format that is more efficient for analytical queries compared to JSON, reducing 1/0 and processing time. Increasing warehouse size (A) might help but is not the most effective strategy. Materialized views (D) are not directly applicable to external tables. Copying all files and creating internal tables is not using the external table functionality (E).
NEW QUESTION # 203
You need to create a development environment from a production schema called 'PRODUCTION SCHEMA. You decide to clone the schema'. Which of the following statements are correct regarding the impact of cloning a schema in Snowflake? (Select all that apply)
A. Cloned schemas consume twice the storage as the source schema immediately after cloning as the underlying data is duplicated.
B. External tables are also cloned when cloning a schema, but the underlying data files in cloud storage are not duplicated.
C. Sequences in the cloned schema will continue from where they left off in the original 'PRODUCTION SCHEMA' if no operations are performed on sequence object, if the sequence is updated after cloning then these sequences are fully independent.
D. All tables, views, and user-defined functions (UDFs) within the 'PRODUCTION_SCHEMX will be cloned to the new development schema.
E. Cloning a schema automatically clones all tasks and streams associated with tables in the schema but only if the clone is executed at the Database Level.
Answer: B,C,D
Explanation:
Cloning a schema clones all objects within it (tables, views, UDFs, etc.). Cloning uses metadata cloning initially, so it doesn't immediately duplicate the data. Sequences resume from where they left off (if not used cloned schema). Tasks and streams are not cloned when cloning a schema, unless the clone is done at the database level. External tables are cloned, but the data files remain in their original cloud storage location.
NEW QUESTION # 204
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