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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Virtual Warehouses | 15% | - Warehouse monitoring and troubleshooting - Multi-cluster warehouses - Cache concepts (warehouse, result, metadata) - Warehouse creation and configuration - Warehouse sizing and scaling |
| Topic 2: Semi-Structured Data | 10% | - Array and object operations - VARIANT data type - Parsing and flattening semi-structured data |
| Topic 3: Security & Access Control | 10% | - Governance best practices - Authentication (SSO, MFA) - Masking policies - Authorization (roles, privileges) - Row-level and column-level security |
| Topic 4: Account Objects | 8% | - Warehouses, databases, and schemas hierarchy - Organizations and accounts structure - Resource monitors |
| Topic 5: Data Loading & Unloading | 15% | - Data ingestion best practices - Continuous loading with Snowpipe - Data unloading and staging - File formats (JSON, CSV, Parquet, AVRO) - Bulk loading with COPY command |
| Topic 6: Databases, Tables & Views | 18% | - Views (standard, materialized, secure) - Column types and sequences - Data manipulation (DML operations) - Table types (permanent, transient, temporary) - Clustering and micro-partitions - Stored procedures and UDFs |
| Topic 7: Data Sharing | 12% | - Data listing marketplace - Shares and reader accounts - Reader account management - Private sharing vs. Data Exchange |
| Topic 8: Time Travel & Cloning | 10% | - Time travel concepts and retention - Fail-safe concepts - UNDROP and historical data access - Zero-copy cloning |
| Topic 9: Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake editions and features - Virtual warehouse concepts and credit usage - Snowflake architecture layers (storage, compute, services) |
| Topic 10: Performance Optimization & Monitoring | 4% | - Query profiling and performance tuning - Resource optimization techniques - Query history and monitoring |
Because the SnowPro® Core Certification 2026 Exam (COF-C03) (COF-C03) test has a restricted time constraint, time management must be exercised to get success. Only with enough practice one can answer real Snowflake COF-C03 Exam Questions in a given amount of time. It has created three formats to aid Snowflake COF-C03 applicants in practicing and organizing their time for this aim.
NEW QUESTION # 196
Which object can be shared from a database in an account with other Snowflake accounts using Secure Data Sharing?
Answer: C
Explanation:
The correct answer is B. Iceberg table .
Snowflake Secure Data Sharing allows providers to share supported database objects with other Snowflake accounts. Supported objects include tables, secure views, secure materialized views, secure UDFs, and certain other supported table types. Snowflake-managed Iceberg tables can be shared in supported configurations.
Why B is correct:
An Iceberg table is a supported table object in Snowflake and can be shared using Secure Data Sharing, subject to Snowflake's supported Iceberg table sharing requirements.
Why the other options are incorrect:
A). Event tables are used to store logging and tracing event data. They are not the expected shareable object in this question.
C). Temporary tables exist only within the session that created them and cannot be shared with other accounts.
D). Stored procedures are not standard objects shared through Secure Data Sharing in the same way as tables and secure views.
Official Snowflake documentation reference:
Snowflake documentation lists supported objects for Secure Data Sharing and describes sharing supported table objects, including Iceberg tables where supported.
Reference: Snowflake Documentation - Secure Data Sharing; Shareable database objects; Iceberg tables; SnowPro Core Study Guide - Data Protection and Governance.
NEW QUESTION # 197
Which REST API can be used with unstructured data?
Answer: B
Explanation:
The REST API used with unstructured data in Snowflake is GET /api/files/, which retrieves (downloads) a data file from an internal or external stage4.
NEW QUESTION # 198
What is the purpose of this Snowflake function? Select TWO.
SYSTEM$PIPE_STATUS( ' < pipe_name > ' )
Answer: B,C
Explanation:
The correct answers are C. To determine if the pipe is suspended and D. To troubleshoot errors in a pipe execution .
SYSTEM$PIPE_STATUS returns status information about a Snowpipe pipe. This function is useful for monitoring Snowpipe health and troubleshooting ingestion issues.
Why C is correct:
The output of SYSTEM$PIPE_STATUS includes information that can indicate whether a pipe is paused or running, helping determine whether ingestion is suspended.
Why D is correct:
The function can return diagnostic information about pipe execution, including pending files, notification channel status, and error-related information. This helps troubleshoot Snowpipe loading problems.
Example:
SELECT SYSTEM$PIPE_STATUS( ' my_pipe ' );
Why the other options are incorrect:
A). The pipe name is supplied as input to the function; the function is not primarily used to identify the pipe name.
B). SYSTEM$PIPE_STATUS is informational only. It does not modify pipe configuration. Configuration changes are made with commands such as ALTER PIPE.
E). Snowpipe loads data into tables. It does not unload data. Listing staged files is done using stage listing commands or directory tables, not SYSTEM$PIPE_STATUS.
Official Snowflake documentation reference:
Snowflake documentation describes SYSTEM$PIPE_STATUS as a system function that retrieves a JSON representation of the current status of a pipe, useful for monitoring and troubleshooting Snowpipe.
Reference: Snowflake Documentation - SYSTEM$PIPE_STATUS; Snowflake Documentation - Snowpipe monitoring and troubleshooting; SnowPro Core Study Guide - Data Loading and Unloading.
NEW QUESTION # 199
When will Snowflake charge credits for the use of the Cloud Services layer?
Answer: C
NEW QUESTION # 200
How can a user validate if a micro-partition is pruning efficiently?
Answer: B
Explanation:
The correct answer is A. By inspecting the statistics pane in the Query Profile .
The Query Profile provides execution details for a query. To evaluate micro-partition pruning, users can inspect scan statistics, including partitions scanned versus total partitions. This helps determine whether Snowflake is efficiently skipping unnecessary micro-partitions.
Why A is correct:
The Query Profile statistics pane can show how many partitions were scanned and how many total partitions existed. Efficient pruning means Snowflake scanned only a small portion of the total relevant micro-partitions.
Example interpretation:
Partitions scanned: 100
Partitions total: 10,000
This indicates strong pruning because Snowflake skipped most micro-partitions.
Why the other options are incorrect:
B). Query details in Snowsight provide useful metadata such as duration, warehouse, and query text, but the Query Profile statistics pane is the best place to validate pruning efficiency.
C). SYSTEM$CLUSTERING_INFORMATION() gives clustering information for a table, such as clustering depth and overlap, but it does not directly show whether a specific query pruned efficiently.
D). INFORMATION_SCHEMA.TABLES provides table metadata, not query-level partition pruning details.
Official Snowflake documentation reference:
Snowflake documentation explains that Query Profile provides detailed execution information, including table scan statistics such as partitions scanned and partitions total. These statistics help validate pruning effectiveness.
Reference: Snowflake Documentation - Query Profile; Snowflake Documentation - Table scan statistics; Snowflake Documentation - Micro-partitions and pruning; SnowPro Core Study Guide - Performance Optimization.
NEW QUESTION # 201
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