A Snowflake database is a top-level container that can include schemas, and within those schemas it can contain objects such astables,views,stages,functions,procedures, anddatabase roles. However,global account-level roles(such as SYSADMIN, SECURITYADMIN, PUBLIC, or custom account roles) do not reside inside a database. These roles are created and managed at the Snowflake account level, not within any specific database. In contrast,database rolesare scoped within a specific database and can exist inside it. Views and tables are standard database objects. Therefore, the only option that a Snowflake database cannot contain is an account-levelRole.
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Question 2
Which of the following is a key feature of Snowflake's Data Storage Layer? (Select 2 options)
Answer : B, C
TheData Storage Layerin Snowflake stores all data in acompressed, encrypted, columnar formatdesigned for analytical efficiency. Snowflake automatically reorganizes ingested data into micro-partitions optimized for pruning and high-performance scans. The compression and structuring are fully automated, requiring no tuning or indexing by the user.
Another foundational feature iselastic storage, made possible by Snowflake's cloud-native architecture. Storage automatically scales up or down based on data volume without user intervention, and is separated from compute. This ensures cost efficiency and immediate availability of capacity for large datasets.
Incorrect options:
In-memory storageis not a Snowflake feature; long-term data is persisted in cloud object storage.
Elastic Cloudis not a Snowflake architectural term.
The Data Storage Layer is designed for durability, scalability, and optimal query performance.
Question 3
Which package is needed for Python worksheet?
Answer : B
Snowflake's Python Worksheets require theSnowpark for Pythonpackage. Snowpark integrates deeply with Snowflake's compute engine, enabling DataFrame operations, UDF creation, ML workflows, and direct execution of Python logic inside Snowflake compute.
While additional libraries like Pandas, NumPy, and Matplotlib may be available in worksheets, they arenot requiredto enable the Python execution environment. Snowpark is the core computational interface that connects Python to Snowflake's data and compute layers.
It enables:
Distributed data processing
Transformations expressed in Python but executed inside Snowflake
Access to Snowflake tables as DataFrames
Integration with Snowpark ML and Cortex functions
Thus, Snowpark is the essential package for Python worksheet support.
Question 4
Snowflake stores semi-structured data in a column of which data type?
Answer : D
TheVARIANTdata type stores semi-structured data (JSON, Parquet, XML, Avro, ORC).
VARIANT allows schema-on-read and supports nested structures.
TEXT/VARCHAR/STRING store plain text only and cannot efficiently query nested structures. STRING is not a Snowflake-native type---VARCHAR is used instead.
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Question 5
What is a benefit of using an external stage to load data into Snowflake?
Answer : A
External stages point to files in external cloud storage (S3, Azure Blob, GCS). Because the data is not stored inside Snowflake, the user avoids Snowflake storage charges, which can significantly reduce cost for large staging datasets.
External stages do not automatically delete files, are not inherently more secure than internal stages, and still count as database objects even though they reference external storage.
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Question 6
When unloading data from a Snowflake table, what is the target destination for the COPY INTO @stage command?
Answer : A
COPY INTO @stage writes query results or table data into files stored in either internal or external stages.
Unloading cannot target a local filesystem directly, cannot load into another table (use INSERT instead), and does not write to streams.
Question 7
Which of the following are benefits of using a multi-clustered warehouse in Snowflake? (Choose any 3 options)
Answer : A, B, D
A multi-cluster warehouse raises Snowflake's capability to handle concurrent workloads efficiently. When large numbers of users or queries hit the system, Snowflake can automatically start additional clusters (scaling out) to distribute workloads, reducing queuing and improving performance. As query demand drops, Snowflake scales in by shutting down clusters to conserve credits. This dynamic auto-scaling provides significantly increased compute capacity during peak usage periods and enhances responsiveness. Importantly, multi-cluster warehouses affect only compute, not storage, so they do not reduce storage costs. Their primary benefits revolve around performance stability, concurrency handling, workload isolation, and compute elasticity.
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