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You are designing a data pipeline using Snowpipe to ingest data from multiple S3 buckets into a single Snowflake table. Each S3 bucket represents a different data source and contains files in JSON format. You want to use Snowpipe's auto-ingest feature and a single Snowpipe object for all buckets to simplify management and reduce overhead. However, each data source has a different JSON schem a. How can you best achieve this goal while ensuring data is loaded correctly and efficiently into the target table?
A. Since Snowpipe cannot handle multiple schemas with a single pipe, pre-process the data in S3 using an AWS Lambda function to transform all files into a common schema before they are ingested by the Snowpipe.
B. Use a single Snowpipe and leverage Snowflake's ability to call a user-defined function (UDF) within the 'COPY INTO' statement to transform the data based on the S3 bucket path. The UDF can parse the bucket path and apply the appropriate JSON schema transformation.
C. Use a single Snowpipe and leverage Snowflake's VARIANT data type to store the raw JSON data. Create separate external tables, each pointing to a specific S3 bucket, and use SQL queries to transform and load the data into the target table.
D. Create a separate Snowpipe for each S3 bucket. Although this creates more Snowpipe objects, it allows you to specify a different FILE FORMAT and transformation logic for each data source.
E. Use a single Snowpipe with a generic FILE FORMAT that can handle all possible JSON schemas. Implement a VIEW on top of the target table to transform and restructure the data based on the source bucket.
Answer: B
Explanation:
The most efficient and manageable approach is to use a single Snowpipe with a UDF to handle schema variations. The UDF can inspect the S3 bucket path (available as metadata within the 'COPY INTO' statement) and apply the correct transformation logic for each data source. Creating separate Snowpipes (A) adds unnecessary overhead. Using a generic 'FILE FORMAT and a VIEW (B) might work for simple transformations, but it becomes complex with significant schema differences. Using VARIANT and external tables (C) defeats the purpose of Snowpipe. Pre-processing in S3 (E) adds complexity outside of Snowflake. UDF provides schema flexibility during ingest and leverages Snowpipe's capabilities directly.
NEW QUESTION # 171
You're tasked with optimizing a Snowflake data pipeline that transforms and loads data into a target table. The pipeline uses a series of complex SQL queries with multiple joins and aggregations. After analyzing the query execution plans, you identify a few key bottlenecks. Which of the following optimization techniques would MOST directly address common performance bottlenecks in such a data pipeline within Snowflake?
A. Disabling the query result cache to ensure that the most up-to-date data is always used.
B. Applying appropriate clustering keys to the target table, ensuring that commonly used filter columns are included in the clustering key definition.
C. Converting all SQL queries to use stored procedures for improved performance and security. Stored procedures execute faster than ad-hoc SQL.
D. Utilizing materialized views to pre-compute and store the results of expensive aggregations, and ensuring the query optimizer rewrites queries to use the materialized views where applicable.
E. Increasing the virtual warehouse size to the largest available option (e.g., X-Large). The bigger the warehouse, the faster all queries will run.
Answer: B,D
Explanation:
Options B and D are the most effective. Clustering keys optimize data retrieval based on filter conditions, significantly improving query performance, and materialized views reduce compute costs by storing pre-computed data and the optimizer rewrites to use it where possible, reducing processing time for queries that require those aggregations. Option A is a brute force approach that may not be cost effective. Option C is generally incorrect because it is not the most cost effecient method. Option E would negatively affect performance.
NEW QUESTION # 172
You are designing a data sharing solution where the consumer account needs real-time access to a secure view that aggregates data from several tables in your provider account. The consumer should not be able to see the underlying tables. Which of the following approaches offers the MOST secure and efficient way to implement this data sharing while minimizing the risk of data leakage and performance impact on your provider account?
A. Create a UDF that encapsulates the data aggregation logic and share the UDF's result using a data share, calling the UDF on demand.
B. Create a materialized view on top of the tables, refresh it periodically, and share the materialized view.
C. Create a standard view that joins the tables and share the view using a data share. Implement row-level security policies on the underlying tables.
D. Create a secure view that joins the tables and share only the secure view using a data share.
E. Create a shared database and grant SELECT privilege on the underlying tables directly to the consumer's role.
Answer: D
Explanation:
Secure views are specifically designed for data sharing while protecting the underlying data sources. Sharing the secure view ensures that the consumer only sees the aggregated data and cannot access the underlying tables directly. Options A and D expose the underlying tables, increasing the risk of data leakage. Option C introduces latency due to the materialized view refresh. Option E adds unnecessary complexity and potential performance overhead.
NEW QUESTION # 173
You are tasked with building a User-Defined Aggregate Function (UDAF) in Snowflake to calculate the weighted average of product prices. The weight is determined by the quantity sold for each product. You have the following table: 'SALES (product_id INTEGER, price quantity INTEGER)'. Which of the following UDAF definitions would correctly calculate the weighted average?
A. Option D
B. Option C
C. Option B
D. Option A
E. Option E
Answer: A
Explanation:
Option D is the correct implementation of a UDAF for calculating the weighted average. It uses a struct to store the sum of (price quantity) and the sum of quantities. It correctly initializes, accumulates, merges, and finalizes the aggregate result. Other options have logical and syntactic errors making it fail. Option A is wrong as it does not provide aggregate function. Option B defines SQL UDF as aggregate functions are called when there are multiple rows, and a single result is desired from those rows, making this definition also wrong. Option C does not account for sum of quanities and uses an unprovided count. Option E also does not account for quanities.
NEW QUESTION # 174
You have implemented a masking policy on the 'SSN' column of the 'EMPLOYEES' table. You now need to suspend the masking policy temporarily for a specific batch job that requires access to the unmasked data'. What is the recommended way to achieve this without dropping the masking policy or altering the user's role?
A. Create a new role with 'ACCOUNTADMIN' privileges, assign this role to the batch job process during the execution, and then revert back to the original role after the job is done.
B. Set the 'DISABLE MASKING' session parameter to 'TRUE' for the batch job session. This will temporarily disable all masking policies.
C. Create a temporary view on the 'EMPLOYEES' table without the 'SSN' column, grant access to the view for the batch job, and drop the view after the job is complete.
D.
E. Grant the 'APPLY MASKING POLICY privilege to the user running the batch job, allowing them to bypass the masking policy.
Answer: D
Explanation:
Option E is the best approach. The function'SYSTEM$GET_PRlVlLEGE MASKING POLICY', helps bypass masking only if the user has such a privilege. Option A is incorrect because it is giving unnecessary privilege and will become permanently effective if it is not revoked. Option B, while technically feasible, is not efficient. Option C is dangerous as ACCOUNTADMIN has too many privileges. Option D disables all masking policies and does not exist in Snowflake.
NEW QUESTION # 175
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