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Snowflake SnowPro Core SnowPro Core Dumps in PDF

Free Snowflake SnowPro Core Real Questions (page: 3)

SnowPro Core PDF — 1300 Questions

Why would a customer size a Virtual Warehouse from an X-Small to a Medium?

  1. To accommodate more queries
  2. To accommodate more users
  3. To accommodate fluctuations in workload
  4. To accommodate a more complex workload

Answer(s): D



True or False: Reader Accounts incur no additional Compute costs to the Data Provider since they are simply reading the shared data without making changes.

  1. True
  2. False

Answer(s): B


Reference:

https://interworks.com/blog/bdu/2020/02/05/zero-to-snowflake-secure-data-sharing/



Which of the following connectors allow Multi-Factor Authentication (MFA) authorization when connecting? (Choose all that apply.)

  1. JDBC
  2. SnowSQL
  3. Snowflake Web Interface (UI)
  4. ODBC
  5. Python

Answer(s): A,B,C,D,E


Reference:

https://docs.snowflake.com/en/user-guide/security-mfa.html



True or False: Snowflake charges a premium for storing semi-structured data.

  1. True
  2. False

Answer(s): B


Reference:

https://snowflakecommunity.force.com/s/question/0D50Z00008ckwNuSAI/does-snowflake-charges-premium-for-storing-semi-structured-data



Which of the following statements describes a benefit of Snowflake's separation of compute and storage? (Choose all that apply.)

  1. Growth of storage and compute are tightly coupled together
  2. Storage expands without the requirement to add more compute
  3. Compute can be scaled up or down without the requirement to add more storage
  4. Multiple compute clusters can access stored data without contention

Answer(s): B,C,D



True or False: It is possible to unload structured data to semi-structured formats such as JSON and Parquet.

  1. True
  2. False

Answer(s): A


Reference:

https://docs.snowflake.com/en/user-guide/data-unload-prepare.html



In which layer of its architecture does Snowflake store its metadata statistics?

  1. Storage Layer
  2. Compute Layer
  3. Database Layer
  4. Cloud Services Layer

Answer(s): D


Reference:

https://hevodata.com/blog/snowflake-architecture-cloud-data-warehouse/



True or False: Data in fail-safe can be deleted by a user or the Snowflake team before it expires.

  1. True
  2. False

Answer(s): B


Reference:

https://blog.knoldus.com/ksnow-time-travel-and-fail-safe-in-snowflake/



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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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