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Snowflake COF-C03 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Loading, Unloading & Connectivity	18%	- External Integrations, Connectors, SnowSQL - Stages, File Formats, COPY INTO, Snowpipe - Unloading Data, Export Options - Bulk Load, Streaming, Dynamic Tables
Topic 2: Performance Optimization, Querying & Transformation	21%	- Query Processing, Profiling, Optimization - Materialized Views, Streams, Tasks, Pipelines - Cortex AI, Notebooks, Apache Iceberg Tables - SQL Transformations, Semi-structured/Unstructured Data
Topic 3: Data Collaboration & Protection	10%	- Data Clean Rooms, Collaboration Features - Data Protection, Retention, Time Travel, Fail-safe - Secure Data Sharing, Listings, Marketplace
Topic 4: Account Management & Data Governance	20%	- Security: Network Policies, MFA, Encryption - Access Control: RBAC, DAC, Roles, Privileges - Trust Center, Data Lineage, Compliance - Governance: Resource Monitors, Data Masking, Row/Column Security
Topic 5: Snowflake Architecture & Core Concepts	31%	- Databases, Schemas, Objects, Data Types - Virtual Warehouses, Clustering, Caching - Snowsight Interface, Core Platform Features - Cloud Services Layer, Compute Layer, Storage Layer

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Snowflake SnowPro® Core Certification 2026 Exam (COF-C03) Sample Questions (Q848-Q853):

NEW QUESTION # 848
What can a user win a reader account do in Snowflake?

A. Update existing data
B. Load now data
C. Query shared data
D. Create a new snare

Answer: C

Explanation:
In Snowflake, a user within a reader account primarily has read-only access to the shared data. Reader accounts are created to enable third parties or separate business units to access and query data shared with them without allowing them to modify the underlying data. This means a user with a reader account can perform operations like querying shared data to analyze or report on it but cannot load new data, update existing data, or create new shares. This setup is crucial for maintaining data governance and security while enabling data sharing and collaboration.
References: Snowflake Documentation on Reader Accounts

NEW QUESTION # 849
What is a key difference between JSON and CSV file formats when loading data? Select TWO.

A. CSV stores flat, tabular data
B. JSON must be converted to CSV before it can be queried
C. CSV files automatically detect and apply data types during loading
D. CSV supports complex data types such as arrays and objects
E. JSON can be loaded into a VARIANT column without predefined schema

Answer: A,E

Explanation:
The correct answers are A. CSV stores flat, tabular data and E. JSON can be loaded into a VARIANT column without predefined schema .
Important correction:
The provided answer A, B is not correct. CSV does not natively support complex semi-structured data types such as arrays and objects. JSON does.
Why A is correct:
CSV is a delimited, flat file format commonly used for structured, tabular data. Each row usually represents a record, and each field is separated by a delimiter such as a comma.
Why E is correct:
JSON is semi-structured data and can be loaded into a Snowflake VARIANT column. This allows Snowflake to store JSON objects and arrays without requiring every nested field to be predefined as a relational column.
Example:
CREATE TABLE json_data (
src VARIANT
);
COPY INTO json_data
FROM @my_stage
FILE_FORMAT = (TYPE = JSON);
Why the other options are incorrect:
B). CSV does not natively support arrays and objects. JSON supports nested structures such as arrays and objects.
C). JSON does not need to be converted to CSV before it can be queried in Snowflake. Snowflake can query JSON data stored in VARIANT, OBJECT, or ARRAY columns.
D). CSV files do not automatically detect and apply Snowflake table data types during loading. The target table schema and file format options control how data is interpreted.
Official Snowflake documentation reference:
Snowflake documentation explains that CSV is used for structured data, while semi-structured formats such as JSON can be loaded into VARIANT columns and queried using semi-structured data syntax.
Reference: Snowflake Documentation - Semi-structured data; Snowflake Documentation - Loading JSON data; Snowflake Documentation - CSV file format options; SnowPro Core Study Guide - Data Loading and Unloading.

NEW QUESTION # 850
Which grant will provide a user with access to data that is in a Snowflake share?

A. GRANT USE ON DATABASE < SHARE > TO ROLE < ROLE > ;
B. GRANT SELECT ON ALL TABLES IN DATABASE < SHARE > TO USER < USER > ;
C. GRANT IMPORTED PRIVILEGES ON DATABASE < SHARE > TO ROLE < ROLE > ;
D. GRANT READ ON ALL TABLES IN DATABASE < SHARE > TO ROLE < ROLE > ;

Answer: C

Explanation:
When a consumer imports a shared database , they must explicitly grant the IMPORTED PRIVILEGES to a role in order to access the shared objects.
Reference:
Snowflake Docs: Accessing Shared Data

NEW QUESTION # 851
A user creates a stage using the following command:

What will be the outcome?

A. An error will be received stating that the storage location for the stage must be identified when creating a stage with a directory table.
B. A stage with a directory table that has metadata that must be manually refreshed will be created.
C. A stage with a directory table set to automatically refresh will be created.
D. The command will fail to run because the name of the directory table is not specified.

Answer: C

NEW QUESTION # 852
Which strategy is recommended to improve the performance of a large, complex query?

A. Add more storage
B. Increase the size of the warehouse
C. Run the query during off-peak hours
D. Convert to a multi-cluster warehouse

Answer: B

Explanation:
To improve the performance of compute-intensive queries, increasing the warehouse size (e.g., from Medium to Large or X-Large) provides more compute resources (CPU/memory) , allowing Snowflake to process queries faster. This is the most direct and effective method for performance optimization.
Reference:
Snowflake Docs: Warehouse Sizing
"Larger warehouses provide more compute resources for faster performance."

NEW QUESTION # 853
......

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