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Snowflake SOL-C01 Exam Syllabus Topics:

Topic
Details

Topic 1

Identity and Data Access Management: This domain focuses on Role-Based Access Control (RBAC) including role hierarchies and privileges, along with basic database administration tasks like creating objects, transferring ownership, and executing fundamental SQL commands.

Topic 2

Data Protection and Data Sharing: This domain addresses continuous data protection through Time Travel and cloning, plus data collaboration capabilities via Snowflake Marketplace and private Data Exchange sharing.

Topic 3

Interacting with Snowflake and the Architecture: This domain covers Snowflake's elastic architecture, key user interfaces like Snowsight and Notebooks, and the object hierarchy including databases, schemas, tables, and views with practical navigation and code execution skills.

Topic 4

Data Loading and Virtual Warehouses: This domain covers loading structured, semi-structured, and unstructured data using stages and various methods, virtual warehouse configurations and scaling strategies, and Snowflake Cortex LLM functions for AI-powered operations.

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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q112-Q117):

NEW QUESTION # 112
You have a table named 'SALES DATA' in your Snowflake account. You want to create a clone of this table for testing purposes, but you also want to mask certain columns containing sensitive customer information (e.g., email addresses, phone numbers) in the cloned table. How can you achieve this with minimal impact on performance and storage?

A. Export the data from 'SALES DATA' to cloud storage, apply masking to the sensitive columns during the export process, and then import the masked data into a new table 'TEST SALES DATA'.
B. Create a clone of 'SALES_DATX using 'CREATE TABLE CLONE SALES_DATA;' then create a task to periodically apply masking to the sensitive data.
C. Create a clone of 'SALES DATA' using 'CREATE TABLE TEST SALES DATA CLONE SALES DATA;' and then create masking policies on the sensitive columns in ` TEST SALES DATA'.
D. Create a view on top of 'SALES DATA' with masking logic for the sensitive columns, and then create a clone of the view as 'TEST SALES DATA'.
E. Create a clone of 'SALES DATA' using 'CREATE TABLE TEST SALES DATA CLONE SALES DATA;' and then manually update the sensitive columns with dummy data.

Answer: C

Explanation:
The most efficient way is to clone the table and then apply masking policies on the cloned table.
Masking policies ensure that data is masked dynamically without altering the underlying data in the original table. This approach minimizes storage usage (due to zero-copy cloning) and performance impact (since masking policies are optimized by Snowflake). Option A is manual and not scalable. Option C involves data export/import which is slower and consumes more resources. Cloning the view doesn't clone underlying data, so it's not suitable for testing changes on data. Option E requires ongoing processing to apply masking periodically.

NEW QUESTION # 113
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 'ON_ERROR = CONTINUE in the COPY INTO command to skip any files with errors.
B. 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.
C. Utilize 'MATCH BY COLUMN NAME = CASE INSENSITIVE so that the command is robust to case differences.
D. Use the 'VALIDATION_MODE = RETURN ERRORS parameter to identify any issues within the load and address them later.
E. Set the 'MAX FILE_SIZE parameter to a smaller value to force Snowflake to process files in smaller chunks.

Answer: B

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 # 114
What is the Snowsight Query Profile used for?

A. To execute SQL queries
B. To create new database objects
C. To manage data loading processes
D. To visualize and analyze query performance

Answer: D

Explanation:
The Snowsight Query Profile is a powerful diagnostic tool that provides a visual breakdown of how Snowflake executed a query. Its primary purpose is to help users visualize and analyze query performance. It displays execution steps, including scan operations, join strategies, pruning results, aggregation methods, and data movement between processing nodes.
The profile shows metrics such as execution time per step, partition pruning effectiveness, bytes scanned, and operator relationships. This allows developers, analysts, and DBAs to identify bottlenecks-such as unnecessary full-table scans, non-selective filters, or inefficient joins-and tune SQL accordingly.
Query Profile does not execute queries; execution happens in worksheets or programmatic interfaces. It does not create objects or manage data loading; those tasks involve separate SQL commands and UI interfaces.
Overall, Query Profile is essential for performance tuning, helping teams reduce compute costs, optimize warehouse sizing, and improve query efficiency.

NEW QUESTION # 115
Role 'DATA ENGINEER' owns the external stage. The security team mandates that ownership be transferred to ADMIN'. Post-transfer, which privileges are automatically revoked or changed for the 'DATA ENGINEER role?

A. Option E
B. Option A
C. Option C
D. Option B
E. Option D

Answer: A

Explanation:
When ownership is transferred, the previous owner (in this case, the 'DATA ENGINEER role) loses the 'OWNERSHIP' privilege. Any other privileges previously granted to the role remain unless explicitly revoked. The ownership transfer itself does not automatically revoke other privileges. The ownership is implicitly revoked. But all other grants (USAGE etc) will remain.

NEW QUESTION # 116
What are computer language considerations when using Snowflake interfaces? (Select TWO).

A. Worksheets can only be written in SQL.
B. Databases can process queries using Scala.
C. Notebook cells can be written in Python.
D. Notebook cells can be written in SQL.
E. Dashboards can be written in JavaScript.

Answer: C,D

Explanation:
Snowflake Notebooks support both SQL and Python as executable cell types. This means that users can create notebook cells written in Python for programmatic data processing and modeling, and in SQL for declarative querying and transformation. Because of this, the statements "Notebook cells can be written in Python" and
"Notebook cells can be written in SQL" are both correct.
Snowflake databases do not natively execute queries written in Scala; Scala is supported via Snowpark APIs for application code, not as a direct query language. Worksheets in Snowsight are primarily SQL-based and also support procedural constructs (e.g., Snowflake Scripting), so the phrase "only in SQL" is not a precise or complete characterization. Dashboards in Snowsight are created using SQL-backed visualizations and built-in UI components; JavaScript is not a supported authoring language inside the native dashboarding layer.
Therefore, options B, D, and E are not correct in this context.

NEW QUESTION # 117
......

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