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Snowflake SOL-C01 Exam Syllabus Topics:

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

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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q95-Q100):

NEW QUESTION # 95
What file extension is commonly used for Snowflake notebooks?

A. .ipnb
B. .ipynb
C. .txt
D. .sql

Answer: B

Explanation:
Snowflake notebooks use the.ipynbfile extension, the standard format for Jupyter notebooks. This format stores executable code, markdown, metadata, and cell outputs in a structured JSON layout. Snowflake adopts this format to ensure compatibility with the broader Python ecosystem, thereby enabling seamless migration between Snowflake and external notebook environments.
The .ipynb structure allows mixed SQL and Python cells, visualizations, Streamlit components, documentation, and stepwise development within Snowsight. It supports reproducibility, collaboration, and integration with Snowpark and Cortex.
Incorrect formats:
* .ipnbis a misspelling and invalid.
* .sqlis used for SQL scripts only.
* .txtcannot represent notebook metadata or cell structure.
Thus, .ipynb is the correct and only supported notebook format.

NEW QUESTION # 96
You have a Snowflake virtual warehouse named 'COMPUTE that is experiencing performance issues during peak hours. The workload consists of a mix of complex analytical queries and high- volume data loading operations. To optimize performance, you want to implement resource monitoring and auto- scaling. Which of the following strategies would be MOST effective?

A. Create separate virtual warehouses for analytical queries and data loading, configure auto-scaling on each warehouse, and use resource monitors to control credit consumption.
B. Implement workload management rules to prioritize analytical queries over data loading operations.
C. Enable auto-suspend on "COMPUTE_WH' to minimize costs during idle periods.
D. Increase the size of 'COMPUTE_WH' to a larger T-shirt size (e.g., from Medium to Large).
E. Create a resource monitor that triggers notifications when the warehouse's credit consumption exceeds a certain threshold.

Answer: A

Explanation:
Option D provides the most effective solution. Separating workloads onto dedicated warehouses allows for independent scaling and optimization. Auto-scaling ensures that each warehouse can adjust its resources dynamically to meet the demands of its specific workload. Resource monitors help control costs and prevent runaway credit consumption. While increasing warehouse size (A) might improve performance, it's not as targeted as workload separation. Resource monitors and auto-suspend (B and C) are helpful but don't address the core issue of workload contention.
Workload management rules (E) can help prioritize, but don't scale resources dynamically.

NEW QUESTION # 97
A Snowflake account has a virtual warehouse named `ANALYTICS WH' configured with multi- clustering. The MIN CLUSTER COUNT is set to 1, and the MAX CLUSTER COUNT is set to 3.
During a period of heavy query load, all three clusters are active. However, after the peak period, the query load decreases significantly. Which of the following statements are TRUE regarding the automatic scaling behavior of this virtual warehouse? (Select TWO)

A. Snowflake will automatically suspend all clusters when the query load drops below a certain threshold.
B. Snowflake will automatically reduce the number of active clusters down to the MIN_CLUSTER_COUNT (1) when the query load decreases.
C. Snowflake will maintain all three clusters indefinitely, regardless of the query load, until the administrator manually scales down the warehouse.
D. The time it takes for Snowflake to reduce the number of clusters depends on the `SCALING POLICY set for the warehouse. `ECONOMY' will scale down faster than 'STANDARD.
E. Snowflake will immediately suspend all idle clusters to minimize credit consumption.

Answer: B,D

Explanation:
Snowflake automatically scales down the number of active clusters in a multi-cluster warehouse when the query load decreases, eventually reducing it to the MIN_CLUSTER_COUNT (1) (B).
The 'SCALING_POLICY determines how aggressively Snowflake scales the warehouse up and down. 'ECONOMY' scales down more aggressively than 'STANDARD' to minimize credit consumption (E). Snowflake will not suspend all clusters entirely if MIN_CLUSTER_COUNT is set to 1 . The clusters are not suspended immediately (C). (D) is incorrect as Snowflake does automatically scale down.

NEW QUESTION # 98
You are using a named file format called to load data from CSV files into a Snowflake table named `PRODUCTS'. However, you are encountering errors during the data loading process because some CSV files contain rows with more columns than defined in the `PRODUCTS table.
You want Snowflake to ignore these extra columns and load the data without errors. Which of the following approaches can achieve this goal?

A. This cannot be achieved directly. Columns number and data types should match exactly
B. In the 'COPY INTO' statement, specify the 'TRUNCATECOLUMNS = TRUE parameter. This option truncates any extra columns.
C. Modify the file format object to include the ` = FALSE parameter.
D. Modify the file format object to include the MISMATCH = TRUE' parameter.
E. In the 'COPY INTO statement, specify the 'MATCH BY COLUMN_NAME' parameter to explicitly map CSV columns to table columns.

Answer: C

Explanation:
Option B is correct. Setting = FALSE in the file format tells Snowflake to ignore extra columns in the CSV files. The default value for this parameter is TRUE, causing an error when the column count does not match. TRUNCATECOLUMNS parameter has nothing to do with the number of columns. It is used to truncate the strings when loading and can cause an error. The MATCH BY_COLIJMN_NAME is used when you have column names defined and they can have a diffent order. The statement in Option E is incorrect

NEW QUESTION # 99
A data warehouse architect is designing a Snowflake environment for a large retail company. The company has diverse data workloads, including batch data ingestion, real-time analytics, and machine learning model training. The architect needs to choose the appropriate Snowflake edition and configure the virtual warehouse sizes to optimize cost and performance. Considering the diverse workloads, which combination of Snowflake edition and virtual warehouse strategy would be most appropriate?

A. Snowflake Standard Edition with separate Small virtual warehouses for batch ingestion and reporting, but use Amazon SageMaker for ML training.
B. Snowflake Standard Edition with a single X-Large virtual warehouse shared across all workloads.
C. Snowflake Virtual Private Snowflake (VPS) with a dedicated virtual warehouse for each department within the retail company.
D. Snowflake Enterprise Edition with separate virtual warehouses of varying sizes (Small to X-Large) for each workload (batch ingestion, real-time analytics, and ML model training). Use resource monitors to control costs.
E. Snowflake Business Critical Edition with a single Medium virtual warehouse that auto-scales based on the workload demand.

Answer: D

Explanation:
Option B is the most appropriate. Enterprise Edition provides the features necessary for diverse workloads (such as materialized views and advanced security), and using separate virtual warehouses allows for workload isolation and optimized resource allocation. Resource monitors can control costs. Standard Edition (A and E) lacks the features required for advanced workloads.
Business Critical Edition (C) is overkill unless specific security or compliance requirements mandate it. VPS (D) is suitable for highly regulated industries but may be more expensive than necessary for a retail company. Using external services such as SageMaker (E) defeats the purpose of leveraging Snowflake for all workloads.

NEW QUESTION # 100
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