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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q269-Q274):

NEW QUESTION # 269
Which two statements describe Snowflake's elasticity features? (Choose two)

A. Snowflake supports semi-structured data formats
B. Storage scales automatically based on demand
C. Storage and compute are tightly coupled for high performance
D. Compute resources can scale independently

Answer: B,D

NEW QUESTION # 270
You have a virtual warehouse named `REPORTING that you want to resize from 'MEDIUM' to
'LARGE. You execute the following SQL command: ALTER WAREHOUSE REPORTING WH SET WAREHOUSE SIZE - LARGE WAIT FOR COMPLETION = TRUE; After executing this command, what will be the state of the warehouse and running queries?

A. The warehouse will be resized to LARGE, and all running queries will continue to run on the MEDIUM warehouse until they complete. New queries will run on the LARGE warehouse.
B. The warehouse will be resized to LARGE, and all running queries will automatically migrate to the new LARGE warehouse size.
C. The command will fail because = TRUE is not a valid option for ALTER WAREHOUSE.
D. The warehouse will be resized to LARGE immediately, and all running queries will be terminated.
E. The warehouse will be resized to LARGE after all running queries complete on the MEDIUM warehouse. No new queries can be submitted until the resize is complete.

Answer: E

Explanation:
When resizing a virtual warehouse with = TRUE , Snowflake will wait for all currently running queries to finish on the old warehouse size before resizing to the new size. New queries will be blocked until the resize is finished. This ensures a clean transition without interrupting existing workloads.

NEW QUESTION # 271
A Snowflake administrator notices that a virtual warehouse named 'REPORTING WH' is frequently idle during off-peak hours. They want to optimize costs by ensuring the warehouse suspends automatically when not in use, but they also need to minimize the latency for the first query after a period of inactivity. Which combination of actions would achieve this goal?

A. Set 'AUTO SUSPEND to a low value (e.g., 60 seconds) and set 'AUTO RESUME to FALSE.
B. Set 'AUTO_SUSPEND' to a low value (e.g., 60 seconds) and ensure AUTO_RESUME is TRUE.
C. Set 'AUTO SUSPEND to NULL and set 'AUTO RESUME to FALSE.
D. Set 'AUTO SUSPEND to a low value (e.g., 60 seconds), ensure `AUTO RESUME is TRUE, and ensure commonly used tables and data are clustered correctly to minimize initial query scan time.
E. Set 'AUTO SUSPEND to a high value (e.g., 3600 seconds) and set `AUTO RESUME to TRUE.
Also, Enable Result Cache.

Answer: B,D

Explanation:
Setting `AUTO SUSPEND to a low value (60 seconds) ensures the warehouse suspends quickly when idle (cost optimization). 'AUTO_RESUME = FALSE (A) would require manual warehouse resumption, increasing latency. Setting 'AUTO_RESUME' to TRUE (B, C, E) allows automatic resumption upon a new query. 'AUTO SUSPEND to a high value(C) defeats the purpose of cost optimization. Setting 'AUTO_SUSPEND' to NULL(D) prevents suspension, negating cost savings.
Reducing latency requires both automatic resumption (AUTO RESIJME = TRUE) AND minimizing the initial query scan time through proper clustering (E), in combination with AUTO RESUME and a low AUTO_SUSPEND time. Result Cache only helps on repeat queries with the same parameters.

NEW QUESTION # 272
What is the purpose of using the SHOW GRANTS ON SQL command for a specific object?

A. To list all databases that have access to the object
B. To list all roles in the account
C. To list all privileges granted on the object
D. To list all users in the account

Answer: C

NEW QUESTION # 273
A data engineer needs to load JSON files containing customer reviews stored in an external stage 's3://my-bucket/reviews/'. The JSON structure varies significantly between files, but the goal is to extract 'review_id', 'customer_id', and 'review text' into a Snowflake table 'CUSTOMER REVIEWS'. The engineer is using a COPY INTO statement with a transform. Which of the following is the MOST efficient and correct way to achieve this?

A. Use a COPY INTO statement with a JSON path expression to extract the required fields during the load. If a field is missing, handle it with 'NULLIF or similar functions during the COPY INTO.
B. Create separate tables for each JSON structure variation and then use a UNION ALL view to combine the data.
C. Use a VARIANT column in 'CUSTOMER_REVIEWS' and load the entire JSON structure. Then, use SQL to extract the required fields during querying.
D. Create a custom file format that defines the expected JSON structure, even though it varies, and rely on Snowflake to handle the variations during the COPY INTO operation.
E. Write a Python UDF to parse the JSON, extract the required fields, and then use the UDF in a COPY INTO statement.

Answer: A

Explanation:
Option C is the most efficient. Using a JSON path expression in the COPY INTO statement allows extracting the desired fields directly during the load, avoiding the need to load the entire JSON and then process it. 'NULLIF or similar functions can be used to handle cases where a field might be missing in some JSON documents. Options A, B, D, and E are less efficient or impractical. Loading the entire JSON (A) requires post-processing. Creating separate tables (B) is complex and doesn't scale well. Python UDF (D) introduces overhead and performance limitations. Relying on a custom format without explicitly handling variations (E) will lead to errors.

NEW QUESTION # 274
......

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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q269-Q274):

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