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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 (Q72-Q77):

NEW QUESTION # 72
You are using Snowsight to analyze query performance. You notice a query that frequently accesses a table 'orders' is slow. Which of the following Snowsight features and actions could you use to identify potential bottlenecks and improve query performance? Select all that apply.

A. Use the 'Advisor' feature in Snowsight to receive recommendations on potential performance improvements, such as creating indexes or optimizing table clustering.
B. Disable query history in Snowsight to reduce the overhead on the system.
C. Use Snowsight's data lineage feature to trace the origin of the `orders' table and identify any upstream transformations that might be causing performance issues.
D. Examine the query profile in Snowsight to identify stages with high execution time and resource consumption.
E. Review the SQL text formatting in the query history to ensure it follows Snowflake's best practices.

Answer: A,D

Explanation:
The query profile in Snowsight provides detailed information about each stage of a query's execution, allowing you to identify bottlenecks. The 'Advisor' feature offers recommendations for performance improvements. Data lineage (Option B) might be helpful in some cases, but it's not the most direct way to address query performance. Disabling query history (Option C) is counterproductive for performance analysis. Option E has no direct impact on query performance.
Only relevant functions in Snowsight related to Snowflake are acceptable.

NEW QUESTION # 73
You are developing a Streamlit application that visualizes data from a Snowflake table called
`CUSTOMER ORDERS. The application needs to display the total order amount for each customer, but the dataset is large. Which of the following Snowflake features, combined with Streamlit's caching capabilities, would be the MOST effective to optimize query performance and reduce costs?

A. Use a Snowflake User-Defined Function (IJDF) written in Python to calculate the total order amount per customer within the Streamlit application.
B. Import the entire table into a Pandas DataFrame within the Streamlit app and perform the aggregation using Pandas functions.
C. Use 'ORDER BY clause in the SQL query to sort the results before sending them to Streamlit.
D. Create a Snowflake external table linked to CSV files containing customer order data and query that table. This allows Streamlit to cache more efficiently.
E. Create a Snowflake View that pre-aggregates the total order amount per customer. Query this view from the Streamlit application, and use `st.cache_data' to cache the results in Streamlit.

Answer: E

Explanation:
Option B is the most effective solution. Creating a Snowflake View pre-aggregates the data, reducing the amount of data transferred and processed. Using 'st.cache_data' then ensures that Streamlit caches the results of the query, preventing unnecessary database calls on subsequent runs. Ordering data (A) doesnt improve performance, external table is not related here (C) , UDF in streamit is expensive and defeats the purpose (D) and loading entire table is not scaleable (E) .

NEW QUESTION # 74
Which layer of the Snowflake architecture performs query optimization and execution?

A. Client Layer
B. Database Storage Layer
C. Cloud Services Layer
D. Query Processing Layer

Answer: D

Explanation:
The Query Processing Layer executes SQL statements using virtual warehouses and massively parallel processing. It handles query optimization, execution, and scaling. The Cloud Services Layer parses and plans queries but does not execute them. Storage persists data, and the Client Layer is external to Snowflake architecture.

NEW QUESTION # 75
Which function in Snowflake Cortex LLM are Task Specific Function? (Select two)

A. CLASSIFY_TEXT / AI_CLASSIFY
B. COUNT_TOKENS
C. TRANSLATE
D. PARSE_DOCUMENT

Answer: A,C,D

Explanation:
Snowflake's Cortex LLM includestask-specific functions, meaning each performs a well-defined AI operation with predictable outputs. Examples include:
* TRANSLATE- Converts text between languages; deterministic and domain-independent.
* CLASSIFY_TEXT / AI_CLASSIFY- Assigns text to predefined categories, ideal for sentiment, topics, or routing tasks.
* PARSE_DOCUMENT- Extracts structured information from documents (PDFs, invoices, receipts, contracts) including layout-aware content.
These functions are optimized for reliability, reproducibility, and governance, making them suitable for production pipelines.
COUNT_TOKENSis not task-specific-it's a utility function used to estimate LLM token usage rather than perform a primary AI task.
Thus, TRANSLATE, CLASSIFY_TEXT, and PARSE_DOCUMENT are the correct task-specific functions.

NEW QUESTION # 76
Which of the following statements are TRUE regarding Snowflake Marketplace listings and data sharing (Select all that apply)?

A. Data sharing between accounts using shares consumes compute resources on the provider's account.
B. Consumers can request a trial period for paid listings before subscribing.
C. A data provider can monetize their data by offering paid listings on the Snowflake Marketplace.
D. Data sharing between accounts using shares consumes compute resources on the consumer's account.
E. All listings on the Snowflake Marketplace are free.

Answer: A,B,C

Explanation:
Paid listings are available on the Snowflake Marketplace, allowing providers to monetize data.
Consumers can often request trial periods for these listings. Standard data sharing leverages the provider's compute for data served from the share. Free listings are available, but not all listings are free. The consumer pays for compute when they create a database or access the data from provider.

NEW QUESTION # 77
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