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| Section | Weight | Objectives |
|---|---|---|
| Account Management & Data Governance | 20% | - Trust Center, Data Lineage, Compliance - Security: Network Policies, MFA, Encryption - Governance: Resource Monitors, Data Masking, Row/Column Security - Access Control: RBAC, DAC, Roles, Privileges |
| Snowflake Architecture & Core Concepts | 31% | - Virtual Warehouses, Clustering, Caching - Cloud Services Layer, Compute Layer, Storage Layer - Snowsight Interface, Core Platform Features - Databases, Schemas, Objects, Data Types |
| Data Collaboration & Protection | 10% | - Data Protection, Retention, Time Travel, Fail-safe - Secure Data Sharing, Listings, Marketplace - Data Clean Rooms, Collaboration Features |
| Performance Optimization, Querying & Transformation | 21% | - Query Processing, Profiling, Optimization - SQL Transformations, Semi-structured/Unstructured Data - Materialized Views, Streams, Tasks, Pipelines - Cortex AI, Notebooks, Apache Iceberg Tables |
| Data Loading, Unloading & Connectivity | 18% | - Unloading Data, Export Options - Stages, File Formats, COPY INTO, Snowpipe - External Integrations, Connectors, SnowSQL - Bulk Load, Streaming, Dynamic Tables |
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NEW QUESTION # 958
What Snowflake objects can contain custom application logic written in JavaScript? (Select TWO)
Answer: C,D
Explanation:
Snowflake allows users to write custom application logic in JavaScript for two types of objects: Stored Procedures and User-Defined Functions (UDFs).
Stored Procedures: Snowflake stored procedures can be written in JavaScript to encapsulate complex business logic and procedural operations.
CREATE OR REPLACE PROCEDURE my_procedure()
RETURNS STRING
LANGUAGE JAVASCRIPT
EXECUTE AS CALLER
AS
$$
// JavaScript logic here
$$;
User-Defined Functions (UDFs): Snowflake UDFs can be written in JavaScript to perform custom calculations or operations on data.
CREATE OR REPLACE FUNCTION my_function(x FLOAT)
RETURNS FLOAT
LANGUAGE JAVASCRIPT
AS
$$
return x * 2;
$$;
References:
Snowflake Documentation: Stored Procedures
Snowflake Documentation: User-Defined Functions (UDFs)
NEW QUESTION # 959
What does Snowflake recommend a user do if they need to connect to Snowflake with a tool or technology mat is not listed in Snowflake partner ecosystem?
Answer: C
Explanation:
If a user needs to connect to Snowflake with a tool or technology that is not listed in Snowflake's partner ecosystem, Snowflake recommends using its JDBC or ODBC drivers. These drivers provide a standard method of connecting from various tools and programming languages to Snowflake, offering wide compatibility and flexibility. By using these drivers, users can establish connections to Snowflake from their applications, ensuring they can leverage the capabilities of Snowflake regardless of the specific tools or technologies they are using.References: Snowflake Documentation on Client Drivers
NEW QUESTION # 960
Which key access control concept does Snowflake descibe as a defined level of access to an object?
Answer: D
Explanation:
In Snowflake, the term "privilege" refers to a defined level of access to an object. Privileges are specific actions that roles can perform on securable objects in Snowflake, such as tables, views, warehouses, databases, and schemas. These privileges are granted to roles and can be further granted to users through their roles, forming the basis of Snowflake's access control framework.References: Snowflake Documentation on Access Control Privileges
NEW QUESTION # 961
How does Snowflake utilize clustering information to improve query performance?
Answer: B
Explanation:
Snowflake utilizes clustering information to enhance query performance by pruning unnecessary micro-partitions.
Clustering Metadata: Snowflake stores clustering information for each micro-partition, which includes data range and distribution.
Pruning Micro-partitions: When a query is executed, Snowflake uses this clustering metadata to identify and eliminate micro-partitions that do not match the query criteria, thereby reducing the amount of data scanned and improving query performance.
References:
Snowflake Documentation on Clustering
Snowflake Documentation on Micro-partition Pruning
NEW QUESTION # 962
Which Snowflake data types can be used to build nested hierarchical data? (Select TWO)
Answer: A,C
Explanation:
The Snowflake data types that can be used to build nested hierarchical data are OBJECT and VARIANT.These data types support the storage and querying of semi-structured data, allowing for the creation of complex, nested data structures
NEW QUESTION # 963
......
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