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Snowflake SnowPro Core SnowPro Core Dumps in PDF

Free Snowflake SnowPro Core Real Questions (page: 25)

SnowPro Core PDF — 1300 Questions

Which Snowflake layer is always leveraged when accessing a query from the result cache?

  1. Metadata
  2. Data Storage
  3. Compute
  4. Cloud Services

Answer(s): D



Which connectors are available in the downloads section of the Snowflake web interface (UI)? (Choose two.)

  1. SnowSQL
  2. JDBC
  3. ODBC
  4. HIVE
  5. Scala

Answer(s): B,C



A Snowflake Administrator needs to ensure that sensitive corporate data in Snowflake tables is not visible to end users, but is partially visible to functional managers.
How can this requirement be met?

  1. Use data encryption.
  2. Use dynamic data masking.
  3. Use secure materialized views.
  4. Revoke all roles for functional managers and end users.

Answer(s): B



Users are responsible for data storage costs until what occurs?

  1. Data expires from Time Travel
  2. Data expires from Fail-safe
  3. Data is deleted from a table
  4. Data is truncated from a table

Answer(s): B



A user has an application that writes a new file to a cloud storage location every 5 minutes.
What would be the MOST efficient way to get the files into Snowflake?

  1. Create a task that runs a COPY INTO operation from an external stage every 5 minutes.
  2. Create a task that PUTS the files in an internal stage and automate the data loading wizard.
  3. Create a task that runs a GET operation to intermittently check for new files.
  4. Set up cloud provider notifications on the file location and use Snowpipe with auto-ingest.

Answer(s): D



What affects whether the query results cache can be used?

  1. If the query contains a deterministic function
  2. If the virtual warehouse has been suspended
  3. If the referenced data in the table has changed
  4. If multiple users are using the same virtual warehouse

Answer(s): C



Which of the following is an example of an operation that can be completed without requiring compute, assuming no queries have been executed previously?

  1. SELECT SUM (ORDER_AMT) FROM SALES;
  2. SELECT AVG(ORDER_QTY) FROM SALES;
  3. SELECT MIN(ORDER_AMT) FROM SALES;
  4. SELECT ORDER_AMT * ORDER_QTY FROM SALES;

Answer(s): C



How many days is load history for Snowpipe retained?

  1. 1 day
  2. 7 days
  3. 14 days
  4. 64 days

Answer(s): C



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A
AI Tutor Explanation
9/24/2026 11:16:10 AM

Question 5:
Correct answer: A — True
Snowflake supports bulk unloading with COPY INTO <location> using a SELECT statement as the source. This allows you to export either:

  • All or selected columns from a table
  • Filtered rows using WHERE
  • Transformed or joined data

Example: 
sql 
COPY INTO @my_stage/export/ 
FROM ( 
  SELECT customer_id, order_date 
  FROM orders 
  WHERE order_date >= '2025-01-01' 
);
Snowflake writes the query results to files in the specified internal or external stage. The key distinction is that COPY INTO is used for bulk movement of data, while a regular SELECT only returns query results and does not unload them to staged files.

Pune, India
A
AI Tutor Explanation
9/3/2026 5:25:59 AM

Question 1:
Correct answer: B — Clustering keys
Snowflake automatically organizes table data into micro-partitions and uses natural clustering based on how data is loaded. However, for large tables where query performance depends on particular columns, you can define a clustering key.
A clustering key:

  • Specifies one or more columns or expressions Snowflake should use when organizing table data.
  • Helps Snowflake’s automatic reclustering keep related rows grouped together.
  • Can improve pruning for queries that frequently filter or join on those columns.

Why the others are incorrect:
  • Micro-partitions: Snowflake’s storage units; users do not directly define them as an override mechanism.
  • Key partitions: Not a Snowflake feature.
  • Clustered partitions: Not the name of the customer-defined mechanism.

The answer key’s B is correct.

Johannesburg, South Africa
A
AI Tutor Explanation
8/28/2026 11:39:56 AM

Question 17:
Correct answer: D — To accommodate a more complex workload.
Increasing a Virtual Warehouse from X-Small to Medium is vertical scaling. The larger warehouse provides more compute resources, which can improve performance for:

  • Complex queries
  • Large scans or joins
  • Resource-intensive transformations
  • Queries involving substantial processing

Why the others are less appropriate:
  • More users / more queries: This usually calls for multi-cluster warehouses, which add clusters to handle concurrency.
  • Fluctuations in workload: This is typically addressed with auto-suspend/auto-resume or multi-cluster scaling, rather than simply choosing a larger warehouse.

Thus, warehouse size primarily affects the compute power available to individual workloads.

Chennai, India
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