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Snowflake COF-C02 Exam Syllabus Topics:

SectionWeightObjectives
Account Access and Security20-25%- Authorization
  • 1. Role Hierarchy
  • 2. Object Privileges
  • 3. Grant/Revoke Commands
- Network Security
  • 1. Network Policies
  • 2. Private Connectivity
- Authentication
  • 1. SSO Integration
  • 2. Multi-Factor Authentication
  • 3. User/Role Management
Performance Optimization20-25%- Query Performance
  • 1. Result Set Caching
  • 2. Query History
  • 3. Query Profiling
- Warehouse Optimization
  • 1. Auto-Suspend/Auto-Resume
  • 2. Multi-cluster Configuration
  • 3. Warehouse Sizing
- Data Optimization
  • 1. Clustering Keys
  • 2. Materialized Views
  • 3. Search Optimization Service
Data Handling and Transformation15-20%- Data Transformation
  • 1. Snowflake SQL Functions
  • 2. Data Pipelines
  • 3. Time Travel Features
- Semi-Structured Data
  • 1. Flattening Data
  • 2. VARIANT Data Type
  • 3. JSON/Avro/Parquet Handling
Cloud Architecture and Data Loading20-25%- Snowflake Architecture Overview
  • 1. Multi-cluster Warehouses
  • 2. Snowflake Editions
  • 3. Virtual Warehouses
  • 4. Layers (Storage, Compute, Services)
- Data Loading Techniques
  • 1. Continuous Loading
  • 2. Bulk Loading (COPY INTO)
  • 3. Data Loading Best Practices
Billing and Account Management10-15%- Account Operations
  • 1. Account Parameters
  • 2. Database/Schema Management
  • 3. Object Naming Conventions
- Credit Usage
  • 1. Credit Monitoring
  • 2. Resource Monitors

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Snowflake SnowPro Core Certification Exam Sample Questions (Q89-Q94):

NEW QUESTION # 89
The following JSON is stored in a VARIANT column called src of the CAR_SALES table:

A user needs to extract the dealership information from the JSON.
How can this be accomplished?

Answer: A

Explanation:
In Snowflake, to extract a specific element from a JSON stored in a VARIANT column, the correct syntax is to use the dot notation. Therefore, the query select src.dealership from car_sales; will return the dealership information contained within each JSON object in the src column.
References: For a detailed explanation, please refer to the Snowflake documentation on querying semi-structured data.


NEW QUESTION # 90
Which data types does Snowflake support when querying semi-structured data? (Select TWO)

Answer: A,D

Explanation:
https://docs.snowflake.com/en/user-guide/semistructured-intro.html#label-loading-semi-structured-data A VARIANT stores semi-structured data in Snowflake. It can store a value of any other type, including OBJECT and ARRAY.
The maximum length of a VARIANT is 16 MB.
A Snowflake ARRAY is similar to an array in many other programming languages. An ARRAY contains 0 or more pieces of data. Each element is accessed by specifying its position in the array.


NEW QUESTION # 91
How does Snowflake describe its unique architecture?

Answer: A


NEW QUESTION # 92
What are the correct parameters for time travel and fail-safe in the Snowflake Enterprise Edition?

Answer: E

Explanation:
In the Snowflake Enterprise Edition, the default Time Travel retention is set to 1 day, the maximum Time Travel retention can be set up to 90 days, and the Fail-safe retention time is 7 days3.


NEW QUESTION # 93
How can a row access policy be applied to a table or a view? (Choose two.)

Answer: A,C

Explanation:
A row access policy can be applied to a table or a view within the policy DDL when defining the policy. Additionally, an existing row access policy can be applied to a table or a view using the ALTER <object> ADD ROW ACCESS POLICY <policy> command


NEW QUESTION # 94
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