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NEW QUESTION # 245
You have a table named 'EVENT LOGS with columns including 'EVENT ID, 'EVENT TIMESTAMP', 'USER ID, 'EVENT_TYPE, and 'EVENT DATA (which stores JSON data). Users frequently query the table filtering by specific key-value pairs within the 'EVENT DATA column. Which of the following approaches will BEST improve query performance when filtering on values inside the JSON column, considering the use of search optimization?
Answer: E
Explanation:
Extracting the frequently queried key-value pairs into separate virtual columns and then enabling search optimization on these columns is the most effective approach (Option B). Snowflake's search optimization works best on columns with well-defined data types. Direct search optimization on a JSON column (Option A) is not directly supported and will not provide the desired performance benefits. Materialized views (Option C) are an option, but virtual columns are generally more lightweight for this scenario. Converting to VARCHAR (Option D) is not the correct approach for JSON data and would prevent proper JSON parsing and filtering. Increasing the warehouse size (Option E) might improve overall performance but doesn't specifically address the JSON filtering bottleneck.
NEW QUESTION # 246
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?
Answer: C,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 # 247
You have a table 'EVENTS' containing application event data with columns 'EVENT ID, 'USER ID, 'EVENT TYPE, and EVENT DETAILS (VARCHAR). The 'EVENT DETAILS column contains comma-separated key-value pairs (e.g., 'location=USA,device=mobile,os=iOS'). Your objective is to transform this structured data into a VARIANT column named EVENT JSON' in a new table 'EVENTS JSON'. The data in EVENT DETAILS has inconsistent key-value pairs across different rows. Which of the following methods are the most efficient and scalable to parse the key-value pairs in 'EVENT DETAILS' and construct the JSON objects?
Answer: A,D
Explanation:
Options B and E are the most efficient because they use Snowflake's built-in functions for parsing the data before constructing the JSON object. 'SPLIT TO_TABLE' (B) and LATERAL FLATTEN (E) are the more scalable way, if the number of key-value pairs might be high in string. While UDFs (A, C, and D) can achieve the transformation, they can be less performant and harder to maintain compared to SQL-based solutions. 'REGEXP EXTRACT ALL' is less efficient in this case as you would still need to iterate through the arrays it creates. Splitting data with 'SPLIT and utilizing LATERAL FLATTEN provides a good balance between performance and readability for handling semi-structured data within Snowflake's SQL environment.
NEW QUESTION # 248
A data engineering team is using Snowflake's data lineage features, and they need to audit changes to data masking policies applied to a table named 'EMPLOYEES'. They want to identify when a masking policy was added, modified, or removed from specific columns.
What are the recommended Snowflake features or audit logs that the data engineering team could use to get these requirements?
Answer: D
Explanation:
The most effective method to audit changes is to combine , showing the current policy assignments, with the 'QUERY HISTORY to identify when those assignments were changed. Specifically, looking for 'ALTER TABLE ... MODIFY COLUMN ... SET MASKING POLICY statements in the 'QUERY_HISTORY will pinpoint the exact changes. Periodically comparing snapshots of 'POLICY_REFERENCES allows you to see which policies were added or removed. Account Usage views track object dependencies, but don't directly show historical changes to policy assignments. Data Lineage focuses on data flow, not policy change auditing. Though has value, it is more appropriate to combine with'QUERY_HlSTORY' since it displays the current masking policies, but will not capture the audit information required without 'QUERY HISTORY and snapshots.
NEW QUESTION # 249
You have a Snowflake stage pointing to an external cloud storage location containing numerous Parquet files. A directory table is created on top of it. Over time, some files are deleted or moved from the external location. You notice discrepancies between the directory table's metadata and the actual files present in the storage location. Choose the option that best describes how Snowflake handles these discrepancies and the actions you should take.
Answer: C
Explanation:
Snowflake's directory tables do not automatically reflect changes in external storage due to file deletions. The 'ALTER DIRECTORY TABLE ... REFRESH' command updates the metadata, but it doesn't automatically remove entries for deleted files. Attempting to load data from a deleted file will likely result in errors. Running 'VALIDATE on the directory table will identify the files that no longer exist in the external stage. Options A, B, C, and D are incorrect because they misrepresent how Snowflake manages changes in the underlying storage.
NEW QUESTION # 250
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