Important Snowflake SOL-C01 Exam Questions

Answer: D

Explanation:

Standard Edition provides all essential Snowflake capabilities:

* SQL data warehousing

* Secure data sharing

* Time Travel (1 day)

* Internal/external stages

* Basic governance features

It does not include HIPAA/PCI compliance (Business Critical), nor unlimited Time Travel, nor advanced private connectivity.

Answer: D

Explanation:

To create a new stage in Snowsight, users navigate through:Data Database Schema Create Stage. Within the schema, selecting the '+' icon opens the option to create a stage. Users may configure internal or external stages, encryption settings, and directory table options. Other paths listed reference creating schemas rather than creating stages and are therefore incorrect.

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Answer: A

Explanation:

The Database Storage Layer is responsible for persistently storing all customer data in Snowflake. This layer automatically manages micro-partitioning, compression, and metadata. Data is stored in optimized, columnar formats inside cloud object storage (AWS S3, Azure Blob Storage, or GCP Storage). Users cannot manipulate physical storage directly; access is only through SQL operations. Other layers serve different purposes: Cloud Services manages metadata and orchestration, Query Processing Layer executes workloads, and Virtual Warehouses provide compute but do not store data.

Answer: B

Explanation:

AUTO_SUSPEND minimizes cost by suspending compute after a specified idle period.

It prevents unnecessary credit consumption while ensuring compute resumes automatically when needed.

Performance improvements depend on warehouse size, not suspension settings.

Answer: D

Explanation:

Changing a Snowflake virtual warehouse from Medium to Large increases the compute resources allocated to that warehouse---such as CPU cores, memory, and I/O bandwidth. This process is calledscaling up, or vertical scaling. It is typically performed to improve performance for complex queries, large joins, or computationally heavy workloads.

Scaling down is the reverse---reducing warehouse size from Large to Medium or similar.

Scaling out refers to adding more clusters to a multi-cluster warehouse, enabling increased concurrency rather than increasing power per cluster.

Scaling in is the reverse of scaling out---reducing the number of active clusters.

Thus, increasing the size from Medium to Large is vertical scaling, also known as scaling up.

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