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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q68-Q73):

NEW QUESTION # 68
You have a Snowflake virtual warehouse named 'ANALYTICS WH' that is used for complex data transformations. You need to ensure that the warehouse automatically suspends itself when it's idle to minimize costs, but also want to ensure it can quickly resume when a query is submitted.
You also want to scale up the warehouse size during peak business hours to handle increased query load. Which of the following actions should you take?

A. Set 'AUTO SUSPEND = 60' and 'AUTO RESUME = TRUE on the warehouse. Schedule a task that runs every hour to execute `ALTER WAREHOUSE ANALYTICS_WH SET WAREHOUSE_SIZE = LARGE during peak hours and back to 'SMALC during off-peak hours.
B. Set 'AUTO SUSPEND = 300' and 'AUTO RESUME = TRUE on the warehouse. Create a scheduled task using Cron to automatically resize the warehouse using 'ALTER WAREHOUSE based on business hours.
C. Set 'AUTO SUSPEND = 3600' and 'AUTO RESUME = FALSE on the warehouse. Manually resize the warehouse using the Snowflake web interface as needed.
D. Set 'AUTO SUSPEND = 60' and 'AUTO RESUME = TRUE on the warehouse. Use Resource Monitors to automatically increase or decrease the warehouse size based on credit consumption during different times of the day.
E. Configure a Resource Monitor with a predefined credit quota and assign it to the `ANALYTICS WY-I' warehouse, allowing it to automatically resize based on the workload.

Answer: B

Explanation:
Option D is the most appropriate solution. Setting to a reasonable value (300 seconds = 5 minutes) ensures cost optimization by automatically suspending the warehouse when idle.
Setting 'AUTO_RESUME = TRUE ensures the warehouse resumes automatically when a query is submitted. Using a scheduled task with SALTER WAREHOUSE' provides programmatic control to resize the warehouse during peak hours. A is mostly correct but uses resource monitor to handle increasing and decreasing the warehouse size, using Scheduled Tasks to do this is more appropriate. B has `AUTO RESUME = FALSE', which is undesirable. C is incorrect since resource monitors cannot increase/decrease warehouse size. E while related to cost management, resource monitors themselves don't handle warehouse resizing dynamically based on workload. Using a task for this with 'ALTER WAREHOUSE provides explicit control.

NEW QUESTION # 69
A data team is designing a data pipeline that loads data from S3 into Snowflake. The raw data in S3 is compressed using gzip and stored in multiple files. They want to use a Snowflake virtual warehouse to perform the data loading. Which of the following COPY INTO command options would be MOST appropriate to optimize the data loading process?

A. Specify 'FILE FORMAT = (TYPE = CSV COMPRESSION = GZIP)' in the COPY INTO command to instruct Snowflake to automatically decompress the gzip files. Also, split the large warehouse for loading and transforming data.
B. Specify 'ON_ERROR = CONTINUE in the COPY INTO command to skip any files with errors.
C. Use the 'VALIDATION_MODE = RETURN ERRORS parameter to identify any issues within the load and address them later.
D. Set the 'MAX FILE_SIZE parameter to a smaller value to force Snowflake to process files in smaller chunks.
E. Utilize 'MATCH BY COLUMN NAME = CASE INSENSITIVE so that the command is robust to case differences.

Answer: A

Explanation:
ERROR = CONTINUE (A) skips errors but doesn't address decompression. 'VALIDATION MODE' (B) is helpful for debugging but doesn't directly optimize the loading process. (C) can ease development but has nothing to do with compression. Specifying = (TYPE = CSV COMPRESSION = GZIP)' (D) allows Snowflake to handle gzip decompression automatically.
'MAX FILE_SIZE (E) can be useful in certain scenarios but is not related to compressed files directly. Splitting the warehouse is not a COPY INTO option, but rather a high-level architecture decision.

NEW QUESTION # 70
In the Query Profile, what does the Pruning section provide?

A. Information on how Snowflake removed columns from the query results.
B. Information on how Snowflake removed objects from the query plan.
C. Information on how Snowflake removed micro-partitions from the query scan.
D. Information on how Snowflake removed rows from the query results.

Answer: C

Explanation:
The Pruning section of the Snowsight Query Profile showshow Snowflake eliminated unnecessary micro- partitions from the scan phaseof the query. Snowflake stores data in micro-partitions and maintains metadata such as min/max values for each column within each partition. When a query includes filters (e.g., WHERE clauses), Snowflake evaluates this metadata to determine which micro-partitions cannot possibly satisfy the predicate. These partitions are skipped, meaning they are never scanned or read from storage.
This process drastically improves performance because Snowflake minimizes I/O, reduces compute usage, and shortens execution time. Partition pruning is especially impactful on large tables because only a fraction of the stored micro-partitions typically need to be accessed.
The Pruning section does not show removedrows-that happens during the filter step. It does not show removedcolumns-column pruning is handled separately by the optimizer. It also does not show removedobjectsfrom the plan. Its sole purpose is to document micro-partition elimination and scan reduction.

NEW QUESTION # 71
What Snowflake parameter is configured in the Query Processing layer?

A. The types of tables available in an account
B. The minimum and maximum micro-partition limits
C. The sizing of virtual warehouses
D. The minimum and maximum serverless compute limits

Answer: C

Explanation:
The Query Processing layer of Snowflake is wherevirtual warehouses operate, so warehouse sizing parameters (X-Small to 6X-Large) fall under this layer. Warehouse size determines compute power, concurrency, and performance behavior for SQL workloads. Administrators configure warehouse size based on workload intensity, response time requirements, and cost considerations.
Serverless compute limits and micro-partition limits belong to storage and services layers. Table types (permanent, transient, temporary) are storage-level configurations, not part of Query Processing.
Thus, warehouse sizing is the correct parameter configured at the Query Processing layer.

NEW QUESTION # 72
How can you search for listings on Snowflake Marketplace? (Choose any 2 options)

A. Contact Snowflake Support
B. Using the search bar at the top of the marketplace
C. Browsing categories and tags
D. User Universal Search

Answer: B,C,D

Explanation:
Snowflake Marketplace provides multiple intuitive discovery mechanisms to help users explore available listings efficiently. Thesearch bar at the top of the Marketplaceis a primary entry point, enabling keyword- based discovery across providers, industries, and dataset types. This facilitates targeted searching for specific topics or data categories.
Universal Search, available in Snowsight, extends search beyond databases and worksheets to include Marketplace listings. This unified search experience allows users to locate datasets without navigating through menus, improving productivity and discoverability.
Browsing categories and tagssupports exploratory search by grouping listings under predefined themes such as financial data, geospatial data, demographics, and more. These tags promote structured navigation, especially useful when users are exploring rather than seeking something specific.
Contacting Snowflake Supportis not required for accessing or discovering listings and is irrelevant to Marketplace search workflows.

NEW QUESTION # 73
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