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Snowflake SOL-C01 Exam Syllabus Topics:

Topic	Details
Topic 1	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 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	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 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 (Q187-Q192):

NEW QUESTION # 187
You are tasked with optimizing a frequently queried table 'SALES DATA' that experiences many updates. Which of the following Snowflake table features can significantly improve query performance in this scenario?

A. Using a Transient table instead of a Permanent table.
B. Partitioning the table on the date column.
C. Using a Temporary table instead of a Permanent table.
D. Clustering the table on the most frequently filtered columns.
E. Creating a View on top of the table.

Answer: D

Explanation:
Clustering is the most effective way to improve query performance for frequently queried tables, especially those with updates. Clustering organizes the physical storage of data based on the clustering keys, allowing Snowflake to efficiently prune data during queries. Views do not affect the storage of the actual data, and transient/temporary tables are generally used for other purpose and will not improve the performence of the queries. Snowflake does not support Partitioning of the table by Users.

NEW QUESTION # 188
A data engineer is experiencing inconsistent query performance during peak hours. They have a single X- Small warehouse currently configured. After analyzing query history, they observed significant queuing delays. Which of the following actions would MOST effectively address this performance issue while minimizing costs in the short term?

A. Enable auto-resume on the warehouse to ensure it's always available.
B. Create a separate X-Small warehouse for each user to isolate their workloads.
C. Enable multi-cluster warehouse with a minimum and maximum cluster count of 2. This will scale out the compute resources as needed.
D. Upgrade the warehouse size to Large. This will provide more resources to handle concurrent queries.
E. Enable auto-suspend on the warehouse to reduce credits consumed during idle periods.

Answer: C

Explanation:
Enabling a multi-cluster warehouse allows Snowflake to automatically scale out compute resources by adding additional clusters as needed to handle concurrent queries and reduce queuing. Upgrading the warehouse size (option A) is more expensive and might not be the most cost-effective initial solution. Auto-suspend/resume (options B & C) do not address concurrency issues. Creating separate warehouses per user (option E) adds administrative overhead and may not be efficient if users have varying workload demands.

NEW QUESTION # 189
Which types of stages are available in Snowflake? (Choose any 2 options)

A. Internal
B. Public
C. Private
D. External

Answer: A,D

Explanation:
Snowflake provides two main types of stages:Internal stagesandExternal stages. Internal stages reside inside Snowflake and include user stages, table stages, and named internal stages. These offer convenient, secure storage within Snowflake's managed environment. External stages reference cloud storage locations such as AWS S3, Azure Blob Storage, or Google Cloud Storage, enabling integration with external data repositories.
Snowflake does not define a stage type called "Public" or "Private," although stages can be granted secure access. Thus, the correct types are Internal and External stages.

NEW QUESTION # 190
Which role is a system defined role in Snowflake?

A. DATA_ENGINEER
B. SNOWFLAKE_DBA
C. SNOWFLAKE_ADMIN
D. USERADMIN

Answer: D

Explanation:
USERADMIN is one of Snowflake'ssystem-defined roles, created automatically in every account. It is responsible for managing users and roles, including CREATE USER, ALTER USER, and role assignment. It is part of Snowflake's default RBAC hierarchy (SYSADMIN, SECURITYADMIN, USERADMIN, etc.).
SNOWFLAKE_ADMIN and SNOWFLAKE_DBA are not Snowflake system roles-they may exist in organizations as custom roles but do not appear by default. DATA_ENGINEER is also user-created and not a built-in role.
Therefore, USERADMIN is the only true system-defined role listed.

NEW QUESTION # 191
A data engineer needs to load JSON files containing customer reviews stored in an external stage 's3://my-bucket/reviews/'. The JSON structure varies significantly between files, but the goal is to extract 'review_id', 'customer_id', and 'review text' into a Snowflake table 'CUSTOMER REVIEWS'. The engineer is using a COPY INTO statement with a transform. Which of the following is the MOST efficient and correct way to achieve this?

A. Use a COPY INTO statement with a JSON path expression to extract the required fields during the load. If a field is missing, handle it with 'NULLIF or similar functions during the COPY INTO.
B. Use a VARIANT column in 'CUSTOMER_REVIEWS' and load the entire JSON structure. Then, use SQL to extract the required fields during querying.
C. Create a custom file format that defines the expected JSON structure, even though it varies, and rely on Snowflake to handle the variations during the COPY INTO operation.
D. Create separate tables for each JSON structure variation and then use a UNION ALL view to combine the data.
E. Write a Python UDF to parse the JSON, extract the required fields, and then use the UDF in a COPY INTO statement.

Answer: A

Explanation:
Option C is the most efficient. Using a JSON path expression in the COPY INTO statement allows extracting the desired fields directly during the load, avoiding the need to load the entire JSON and then process it. 'NULLIF or similar functions can be used to handle cases where a field might be missing in some JSON documents. Options A, B, D, and E are less efficient or impractical. Loading the entire JSON (A) requires post-processing. Creating separate tables (B) is complex and doesn't scale well. Python UDF (D) introduces overhead and performance limitations. Relying on a custom format without explicitly handling variations (E) will lead to errors.

NEW QUESTION # 192
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