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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Account Management & Data Governance | 20% | - Access Control: RBAC, DAC, Roles, Privileges - Security: Network Policies, MFA, Encryption - Trust Center, Data Lineage, Compliance - Governance: Resource Monitors, Data Masking, Row/Column Security |
| Topic 2: 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 |
| Topic 3: Performance Optimization, Querying & Transformation | 21% | - Query Processing, Profiling, Optimization - Materialized Views, Streams, Tasks, Pipelines - Cortex AI, Notebooks, Apache Iceberg Tables - SQL Transformations, Semi-structured/Unstructured Data |
| Topic 4: Data Collaboration & Protection | 10% | - Data Protection, Retention, Time Travel, Fail-safe - Secure Data Sharing, Listings, Marketplace - Data Clean Rooms, Collaboration Features |
| Topic 5: Data Loading, Unloading & Connectivity | 18% | - Unloading Data, Export Options - Stages, File Formats, COPY INTO, Snowpipe - Bulk Load, Streaming, Dynamic Tables - External Integrations, Connectors, SnowSQL |
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NEW QUESTION # 313
Which statements are correct concerning the leveraging of third-party data from the Snowflake Data Marketplace? (Choose two.)
Answer: B,D
Explanation:
When leveraging third-party data from the Snowflake Data Marketplace, the data is live, ready-to-query, and can be personalized.Additionally, the data is available without the need for copying or moving it to an individual Snowflake account, allowing for seamless integration with existing data
NEW QUESTION # 314
Which command should be used to load data incrementally based on column values that are specified in the source table or subquery?
Answer: D
Explanation:
The MERGE command in Snowflake is used for incremental loading based on column values in a source table or subquery. It enables the insertion, updating, or deletion of records in a target table depending on whether matching rows are found, making it ideal for loading data that changes incrementally, such as daily updates or modifications.
NEW QUESTION # 315
If a column contains float values that are greater than (15,9) and the values need to be preserved when unloading data, what data file format should be used?
Answer: A
Explanation:
The correct answer is A. JSON .
When unloading floating-point or high-precision numeric values, users must consider whether the target file format can preserve the values as needed. JSON stores values in a textual semi-structured representation and can preserve numeric values without forcing them into a fixed Parquet numeric precision representation.
Why A is correct:
For values that exceed a precision/scale such as (15,9) and must be preserved during unloading, JSON is the safest option among the listed choices because it can represent the values textually in the output.
Why the other options are incorrect:
B). PARQUET has limitations for certain numeric precision/scale representations during unload. Snowflake documentation notes precision-related considerations for Parquet unloads.
C). AVRO is a semi-structured file format, but it is not the best documented answer for preserving float values beyond this precision scenario.
D). ORC is not the best answer for this Snowflake unload precision scenario.
Official Snowflake documentation reference:
Snowflake documentation for unloading data explains that file format choice can affect how numeric values are represented and preserved. JSON is commonly used when preserving semi-structured or text-based representations is required.
Reference: Snowflake Documentation - Unloading data; Snowflake Documentation - File format considerations; SnowPro Core Study Guide - Data Loading and Unloading.
NEW QUESTION # 316
What is the default access of a securable object until other access is granted?
Answer: D
Explanation:
In Snowflake, the default access level for any securable object (such as a table, view, or schema) is " No access " until explicit access is granted. This means that when an object is created, only the owner of the object and roles with the necessary privileges can access it. Other users or roles will not have any form of access to the object until it is explicitly granted.
This design adheres to the principle of least privilege, ensuring that access to data is tightly controlled and that users and roles only have the access necessary for their functions. To grant access, the owner of the object or a role with the GRANT option can use the GRANT statement to provide specific privileges to other users or roles.
For example, to grant SELECT access on a table to a specific role, you would use a command similar to:
GRANTSELECTONTABLEmy_tableTOROLE my_role;
Reference: Snowflake Documentation on Access Control (https://docs.snowflake.com/en/user-guide/security- access-control-overview.html)
NEW QUESTION # 317
What are benefits of using Snowpark with Snowflake? (Select TWO).
Answer: C,E
Explanation:
Snowpark is designed to bring the data programmability to Snowflake, enabling developers to write code in familiar languages like Scala, Java, and Python. It allows for the execution of these codes directly within Snowflake's virtual warehouses, eliminating the need for a separate cluster.Additionally, Snowpark's compatibility with Spark allows users to leverage their existing Spark code with minimal changes1.
NEW QUESTION # 318
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