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Snowflake SOL-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Identity and Data Access Management	15%	- Define the roles that are used in Snowflake 1. Privileges 2. Role types 3. Role-Based Access Control (RBAC) 4. Object access by role 5. Explain role hierarchy - Create a database, explore data, configure parameters, and transfer ownership 1. Create and drop schemas 2. Run basic SQL commands (EXCLUDE, SELECT, LIMIT) 3. Use database objects (INFORMATION_SCHEMA, PUBLIC SCHEMA) 4. Understand context 5. Transfer ownership
Topic 2: Data Loading and Virtual Warehouses	40%	- Create tables in Snowflake and load data into the tables 1. View the table definition 2. Use INSERT statements to load data 3. Use COPY INTO statements 4. Use Snowsight to identify particular tables 5. Preview the table data 6. Use Snowsight to load data 7. File format options - Explain how to use Snowflake Cortex LLM functions 1. PARSE_DOCUMENT function 2. CLASSIFY_TEXT function 3. TRANSLATE function 4. COMPLETE function - Explain how to work with unstructured data 1. Use of Pre-signed URLs 2. Function argument 3. Directory tables (Enable, Use of SELECT statements)
Topic 3: Data Protection and Data Sharing	10%	- Outline continuous data protection with Snowflake 1. Cloning 2. Time Travel - Define Snowflake data sharing capabilities 1. Snowflake Marketplace (Search)
Topic 4: Interacting with Snowflake and the Architecture	35%	- Work with Snowflake Notebooks 1. Python variable substitution 2. Notebook sessions 3. Run code (SQL, Python, Cell execution status) 4. Visualize data using Streamlit - Outline key features and benefits of the Snowflake AI Data Cloud 1. Snowflake layers 2. Elastic compute 3. Elastic storage - Describe how to use the Snowsight user interface 1. Query history 2. Object browsers 3. Create objects (e.g., databases, schemas, stages) 4. Data loading - Outline the key Snowflake user interfaces 1. Snowsight 2. Snowflake Notebooks 3. Worksheets (Python, SQL) - Describe Snowflake objects and how they fit into the Snowflake hierarchy 1. Schemas 2. Databases 3. Tables 4. Data types 5. Views

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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q20-Q25):

NEW QUESTION # 20
You are using PARSE DOCUMENT on documents stored in an external stage. You notice that some documents are skipped during processing without any explicit error messages. Which of the following could be the MOST likely reason(s) for this behavior, and how would you address it?

A. The documents are in an unsupported format. Ensure that the documents are in a supported format (PDF, DOCX, etc.) and that Snowflake has the necessary permissions to access the external stage. Check the 'VALIDATE function to verify if the data file is valid before parsing.
B. The documents are too large. Increase the virtual warehouse size and set the MAX FILE SIZE parameter in the file format to a higher value (e.g., 16MB), and retry the operation.
C. Snowflake encountered an internal error during processing and silently skipped the document.
Review the Snowflake query history for error messages or contact Snowflake support for assistance.
D. The documents do not contain any text. Verify that the documents actually contain readable text and are not just images or empty files. Remove the files without text
E. The virtual warehouse is being automatically suspended due to inactivity. Configure auto-resume for the virtual warehouse to ensure it remains active during processing. Also, explicitly specify the virtual warehouse to use by using USE WAREHOUSE

Answer: A,E

Explanation:
Option B is a common cause, where unsupported format and permissions are key things to consider and `VALIDATE' helps ensure data is valid. Option C is also correct, because virtual warehouse suspension can interrupt processing and specifying the warehouse ensures it's used.
Option A, while relevant to performance, doesn't explain silent skipping unless the files grossly exceed limits. Option D is possible but less likely than B or C and harder to diagnose without specific error messages. Option E is less likely, because even if the content is not parseable, it still doesn't explain why document is skipped silently.

NEW QUESTION # 21
You are using Snowsight to monitor the performance of several queries. You notice a query that took significantly longer than expected.
Which of the following actions, available directly within the Snowsight Query History Details panel, would BEST help you diagnose the cause of the slow performance?

A. Examine the SQL text of the query and identify potential areas for optimization based on syntax.
B. Review the 'Query Profile' to identify specific execution stages that consumed the most resources.
C. Download the query results to your local machine for further analysis.
D. Analyze the 'Compilation Time' to determine if query compilation contributed significantly to the overall execution time.
E. Check the 'Warehouse Load' graph to see if the warehouse was under heavy load during the query execution.

Answer: B,D,E

Explanation:
The Query Profile provides a detailed breakdown of the query execution, allowing you to pinpoint specific stages that are slow. Checking the Warehouse Load helps determine if resource contention was a factor. Analyzing the Compilation Time reveals if the query spent a significant amount of time compiling before execution. Examining the SQL text directly (option A) can be helpful, but the Query Profile provides more granular performance data within Snowsight.
Downloading results (option E) does not directly aid in diagnosing performance issues within Snowflake.

NEW QUESTION # 22
What type of mode available in PARSE_DOCUMENT function? (choose two)

A. LAYOUT
B. CONTENT
C. OCR
D. OMR

Answer: A,C

Explanation:
PARSE_DOCUMENT supports two processing modes:OCRandLAYOUT. OCR mode performs Optical Character Recognition, extracting raw text from scanned documents, images within PDFs, or low-quality text- based documents. It is ideal for scenarios like contract ingestion, receipt processing, or older scanned documents. LAYOUT mode extracts structured layout elements-tables, paragraphs, lines, bounding boxes- preserving the original document's spatial organization. This enables downstream analytical tasks such as table reconstruction or semantic segmentation of document content. OMR (Optical Mark Recognition) is not a supported feature, and "CONTENT" is not a valid mode. By supporting OCR and LAYOUT modes, Snowflake Cortex provides robust document intelligence capabilities directly within the Snowflake environment.

NEW QUESTION # 23
You are working with a Snowflake table named 'transactions' that contains a 'transaction_time' column of data type 'TIMESTAMP NTZ'. You need to retrieve all transactions that occurred within the last 24 hours, and you want to optimize this query for performance. Which of the following approaches would be the MOST efficient?

A. SELECT FROM transactions WHERE transaction_time - INTERVAL '24 hours'
B. SELECT FROM transactions WHERE transaction_time CONVERT TIMEZONE('UTC',
'America/Los_Angeles', CURRENT TIMESTAMP()) - INTERVAL '24 hours';
C. SELECT FROM transactions WHERE transaction_time BETWEEN - INTERVAL '24 hours' AND
D. SELECT FROM transactions WHERE transaction_time DATEADD(hour, -24,
E. SELECT FROM transactions WHERE transaction_time SYSDATE() - (24/24);

Answer: A

Explanation:
Option B is the most efficient and recommended approach. Using 'CURRENT TIMESTAMP() - INTERVAL '24 hours" directly leverages Snowflake's internal time handling and avoids unnecessary function calls. Option A will work, but 'DATEADD& function might be slower compared to subtracting the interval directly- Option C is less performant because BETWEEN requires calculation of both upper and lower bounds, adding unnecessary computatiom Option D includes timezone conversion, which introduces overhead and is not necessary if the
`transaction_time' is already in UTC or if the timezone doesn't matter. Option E, SYSDATE() - (24/24)' will produce same results since it gets the current timestamp and subtract one day's worth of data from it As the value is still relative to the timezone of the system, not UTC, and the subtraction might not be optimized for performance like 'INTERVAL' , making it less ideal.

NEW QUESTION # 24
A data warehouse contains a table 'ORDERS' with columns 'ORDER ID', 'CUSTOMER ID',
'ORDER DATE, and `ORDER TOTAL' You need to optimize a query that frequently retrieves the total order amount for a specific customer within a given date range. Which of the following strategies would be MOST effective in improving query performance?

A. Increase the warehouse size to allow for more compute resources.
B. Create a materialized view that pre-calculates the total order amount for each customer.
C. create an index on the 'ORDER DATE column.
D. Cluster the `ORDERS' table using `CUSTOMER ID and `ORDER DATES.
E. Create a view that filters the `ORDERS' table based on the date range.

Answer: D

Explanation:
Clustering the table on `CUSTOMER ID and 'ORDER DATE is the most effective strategy because it physically organizes the data on disk based on these columns. This allows Snowflake to efficiently retrieve the relevant data for a specific customer and date range, minimizing the amount of data that needs to be scanned. Increasing warehouse size will improve general performance, but clustering optimizes for this specific query. Indexes are not typically used in Snowflake. A materialized view would be good, however, clustering is more effective.

NEW QUESTION # 25
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