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Snowflake COF-C03 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Account Management & Data Governance20%- Trust Center, Data Lineage, Compliance
- Access Control: RBAC, DAC, Roles, Privileges
- Governance: Resource Monitors, Data Masking, Row/Column Security
- Security: Network Policies, MFA, Encryption
Topic 2: Snowflake Architecture & Core Concepts31%- Databases, Schemas, Objects, Data Types
- Virtual Warehouses, Clustering, Caching
- Cloud Services Layer, Compute Layer, Storage Layer
- Snowsight Interface, Core Platform Features
Topic 3: Performance Optimization, Querying & Transformation21%- Cortex AI, Notebooks, Apache Iceberg Tables
- Query Processing, Profiling, Optimization
- SQL Transformations, Semi-structured/Unstructured Data
- Materialized Views, Streams, Tasks, Pipelines
Topic 4: Data Loading, Unloading & Connectivity18%- Stages, File Formats, COPY INTO, Snowpipe
- External Integrations, Connectors, SnowSQL
- Unloading Data, Export Options
- Bulk Load, Streaming, Dynamic Tables
Topic 5: Data Collaboration & Protection10%- Secure Data Sharing, Listings, Marketplace
- Data Clean Rooms, Collaboration Features
- Data Protection, Retention, Time Travel, Fail-safe

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Snowflake SnowPro® Core Certification 2026 Exam (COF-C03) Sample Questions (Q1073-Q1078):

NEW QUESTION # 1073
Which feature should be used for near-real-time data ingestion, ensuring that data is available for analysis almost immediately?

Answer: D

Explanation:
Snowpipe Streaming is designed for continuous, low-latency, row-based ingestion directly into Snowflake without first writing files to a stage. Its high-performance architecture can make ingested data available for querying within seconds, which supports near-real-time dashboards, monitoring, and analytics. Traditional Snowpipe continuously loads staged files in micro-batches and generally makes them available within minutes. A manually issued COPY INTO < table > command is a bulk-loading operation, while a stream records change-data-capture offsets and does not ingest external data by itself. Because the requirement emphasizes almost immediate availability and near-real-time ingestion feedback, Snowpipe Streaming is the most appropriate option. It combines managed ingestion infrastructure with low latency and scalable throughput.


NEW QUESTION # 1074
When is a DataFrame processed in Snowpark?

Answer: B

Explanation:
The correct answer is B. When a collect() action is triggered .
Snowpark DataFrames are evaluated lazily. Creating a DataFrame or applying transformations does not immediately execute the query in Snowflake. Execution happens only when an action is called.
Why B is correct:
collect() is an action. When it is called, Snowpark sends the accumulated DataFrame logic to Snowflake for execution and returns the results to the client.
Example:
df = session.table( " CUSTOMERS " )
filtered_df = df.filter(df[ " REGION " ] == " WEST " )
# Query is processed when this action runs:
rows = filtered_df.collect()
Why the other options are incorrect:
A). Creating a DataFrame builds a logical plan but does not process the data.
C). Warehouse memory availability is not what triggers DataFrame execution.
D). Applying a transformation adds to the logical plan, but it does not execute the plan.
Official Snowflake documentation reference:
Snowflake documentation describes Snowpark DataFrames as lazily evaluated. Transformations are not executed until an action, such as collect(), is called.
Reference: Snowflake Documentation - Snowpark DataFrames; Snowflake Documentation - Snowpark actions and transformations; SnowPro Core Study Guide - SQL and Snowflake Objects.
==


NEW QUESTION # 1075
Which Query Profile metrics will provide information that can be used to improve query performance?
(Select TWO).

Answer: B,E

Explanation:
Two key metrics in Snowflake'sQuery Profilethat provide insights for performance improvement are:
Remote Disk IO: This measures the time the query spends waiting on remote disk access, indicating potential performance issues related to I/O bottlenecks.
Pruning: This metric reflects how effectively Snowflake's micro-partition pruning is reducing the data scanned. Better pruning (more partitions excluded) leads to faster query performance, as fewer micro- partitions need to be processed.
These metrics are essential for identifying and addressing inefficiencies in data retrieval and storage access, optimizing overall query performance.


NEW QUESTION # 1076
Which object can be referenced from a User-Defined Function, or UDF?

Answer: C

Explanation:
The correct answer is A. View.
In Snowflake, a SQL User-Defined Function can reference certain database objects in a SQL expression or query expression. A view can be referenced by a SQL UDF, subject to normal privilege and ownership rules.
Why A is correct:
A UDF can include SQL logic that references a view. This allows reusable logic to be built on top of existing query definitions.
Example concept:
CREATE FUNCTION my_function()
RETURNS NUMBER
AS
$$
SELECT COUNT(*) FROM my_view
$$;
Why the other options are less appropriate:
B). A stream is a change tracking object and is not the standard object referenced from a UDF for reusable query logic.
C). An external table is used to query data stored in external cloud storage. It is not the best answer for this SnowPro-style question.
D). A materialized view is a precomputed view-like object, but the standard documented answer for an object referenced from a UDF is a view.
Official Snowflake documentation reference:
Snowflake documentation for SQL UDFs explains that SQL UDF definitions can contain SQL expressions and query expressions. These query expressions can reference database objects such as views, depending on privileges and valid SQL usage.
Reference: Snowflake Documentation - SQL user-defined functions; Snowflake Documentation - Views; SnowPro Core Study Guide - SQL and Snowflake Objects.
==


NEW QUESTION # 1077
Dynamic Data Masking can be used with which Snowflake objects? (Select TWO)

Answer: A,B

Explanation:
Dynamic Data Masking (DDM) can be applied to:
* Tables
* Views
It cannot be applied to materialized views or external tables as those do not support the runtime evaluation of masking policies.
Reference:
Snowflake Docs: Dynamic Data Masking


NEW QUESTION # 1078
......

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