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Snowflake COF-C03 Exam Syllabus Topics:

SectionWeightObjectives
Security & Access Control10%- Masking policies
- Authentication (SSO, MFA)
- Authorization (roles, privileges)
- Governance best practices
- Row-level and column-level security
Time Travel & Cloning10%- Zero-copy cloning
- UNDROP and historical data access
- Time travel concepts and retention
- Fail-safe concepts
Account Objects8%- Warehouses, databases, and schemas hierarchy
- Organizations and accounts structure
- Resource monitors
Semi-Structured Data10%- VARIANT data type
- Array and object operations
- Parsing and flattening semi-structured data
Virtual Warehouses15%- Cache concepts (warehouse, result, metadata)
- Warehouse monitoring and troubleshooting
- Warehouse creation and configuration
- Multi-cluster warehouses
- Warehouse sizing and scaling
Data Loading & Unloading15%- Data ingestion best practices
- Continuous loading with Snowpipe
- Data unloading and staging
- Bulk loading with COPY command
- File formats (JSON, CSV, Parquet, AVRO)
Databases, Tables & Views18%- Table types (permanent, transient, temporary)
- Views (standard, materialized, secure)
- Clustering and micro-partitions
- Data manipulation (DML operations)
- Stored procedures and UDFs
- Column types and sequences
Performance Optimization & Monitoring4%- Resource optimization techniques
- Query history and monitoring
- Query profiling and performance tuning
Data Sharing12%- Shares and reader accounts
- Reader account management
- Private sharing vs. Data Exchange
- Data listing marketplace
Cloud Platform Overview & Snowflake Key Concepts8%- Snowflake architecture layers (storage, compute, services)
- Snowflake editions and features
- Virtual warehouse concepts and credit usage

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Snowflake SnowPro® Core Certification 2026 Exam (COF-C03) Sample Questions (Q46-Q51):

NEW QUESTION # 46
What tasks can be completed using the copy command? (Select TWO)

Answer: A,B

Explanation:
The COPY command in Snowflake allows for the reordering of columns as they are loaded into a table, and it also permits the omission of columns from the source file during the load process. This provides flexibility in handling the schema of the data being ingested. References: [COF-C02] SnowPro Core Certification Exam Study Guide


NEW QUESTION # 47
Which function should be used to insert JSON format string data inot a VARIANT field?

Answer: D

Explanation:
To insert JSON formatted string data into a VARIANT field in Snowflake, the correct function to use is PARSE_JSON. The PARSE_JSON function is specifically designed to interpret a JSON formatted string and convert it into a VARIANT type, which is Snowflake's flexible format for handling semi-structured data like JSON, XML, and Avro. This function is essential for loading and querying JSON data within Snowflake, allowing users to store and manage JSON data efficiently while preserving its structure for querying purposes. This function's usage and capabilities are detailed in the Snowflake documentation, providing users with guidance on how to handle semi-structured data effectively within their Snowflake environments.
References:
Snowflake Documentation: PARSE_JSON


NEW QUESTION # 48
A user needs to set up a virtual warehouse for the organization's BI team. The team has around 14 users running similar queries at the same time, and queries often get queued. Which virtual warehouse configuration should be assigned to the team to improve performance and concurrency?

Answer: B

Explanation:
Queued queries from many concurrent BI users indicate a concurrency issue. A multi-cluster warehouse improves concurrency by adding clusters as query demand increases. Increasing warehouse size helps individual query speed, but adding clusters is better for concurrent query queuing.
Reference: Snowflake Documentation - Multi-cluster warehouses; Warehouse load and concurrency.
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NEW QUESTION # 49
Which statement will trigger a stream to advance its offset?

Answer: B


NEW QUESTION # 50
How can performance be optimized for a query that returns a small amount of data from a very large base table?

Answer: A

Explanation:
The search optimization service in Snowflake is designed to improve the performance of selective point lookup queries on large tables, which is ideal for scenarios where a query returns a small amount of data from a very large base table1. References: [COF-C02] SnowPro Core Certification Exam Study Guide


NEW QUESTION # 51
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