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| Section | Weight | Objectives |
|---|---|---|
| Snowflake Architecture & Core Concepts | 31% | - Cloud Services Layer, Compute Layer, Storage Layer - Snowsight Interface, Core Platform Features - Databases, Schemas, Objects, Data Types - Virtual Warehouses, Clustering, Caching |
| Data Collaboration & Protection | 10% | - Secure Data Sharing, Listings, Marketplace - Data Protection, Retention, Time Travel, Fail-safe - Data Clean Rooms, Collaboration Features |
| Account Management & Data Governance | 20% | - Security: Network Policies, MFA, Encryption - Trust Center, Data Lineage, Compliance - Governance: Resource Monitors, Data Masking, Row/Column Security - Access Control: RBAC, DAC, Roles, Privileges |
| Data Loading, Unloading & Connectivity | 18% | - External Integrations, Connectors, SnowSQL - Bulk Load, Streaming, Dynamic Tables - Stages, File Formats, COPY INTO, Snowpipe - Unloading Data, Export Options |
| Performance Optimization, Querying & Transformation | 21% | - SQL Transformations, Semi-structured/Unstructured Data - Materialized Views, Streams, Tasks, Pipelines - Cortex AI, Notebooks, Apache Iceberg Tables - Query Processing, Profiling, Optimization |
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NEW QUESTION # 787
A user executes a long-running query at the same time every morning. A second user with the same role needs to access the query results for additional data processing and analysis. How should the results be reproduced for the second user?
Answer: B
Explanation:
RESULT_SCAN can return the result set of a previous query when the query ID is supplied, provided the result is still available and the user has appropriate access. Since the second user has the same role, locating the query ID and using RESULT_SCAN is the most efficient way to reproduce the results without rerunning the long query.
Example:
SELECT *
FROM TABLE(RESULT_SCAN( ' < query_id > ' ));
Reference: Snowflake Documentation - RESULT_SCAN; Persisted query results; QUERY_HISTORY.
NEW QUESTION # 788
What task can a data consumer perform using third-party datasets from the Snowflake Marketplace?
Answer: D
Explanation:
Snowflake Marketplace allows consumers to discover and access third-party datasets without building separate ingestion pipelines for each provider. Consumers can combine Marketplace data with data already stored in their Snowflake environment and query the combined information to produce new analytics and business insights. The provider continues to control and update the shared data product, while the consumer generally receives read-only access through a listing. Consumers cannot modify the provider's underlying schemas or source data, and imported shared databases cannot be re-shared as the consumer's own listing. Exporting data to local files is not the defining Marketplace capability described in this question. Therefore, integrating third-party Marketplace data with the consumer's existing Snowflake data is the supported task represented by option B.
NEW QUESTION # 789
When is a DataFrame processed in Snowpark?
Answer: A
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 # 790
What metrics will the show tables command in Snowsight provide?
Answer: C
NEW QUESTION # 791
A user needs to connect a private GitHub repository to Snowflake for stored-procedure version control. The user's organization requires secure authentication.
Which authentication method should be configured for access to the private GitHub repository?
Answer: D
Explanation:
For a private GitHub repository accessed over HTTPS, Snowflake can authenticate using a GitHub personal access token. The token is stored securely in a Snowflake secret rather than written directly into the Git repository object.
The secret is referenced by the API integration or Git repository configuration. For GitHub, a personal access token is generally supplied in the password field of a password-type Snowflake secret.
A normal GitHub account password is not the recommended authentication mechanism for Git operations.
Interactive MFA cannot be performed by Snowflake when it synchronizes a repository. An arbitrary OAuth token is not the standard answer for the documented private-GitHub setup represented by this question.
References:
Snowflake Documentation - Setting up Snowflake to use Git: https://docs.snowflake.com/en/developer-guide
/git/git-setting-up
Snowflake Documentation - Using a Git repository in Snowflake: https://docs.snowflake.com/en/developer- guide/git/git-overview
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NEW QUESTION # 792
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