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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Account Management & Data Governance | 20% | - Governance: Resource Monitors, Data Masking, Row/Column Security - Security: Network Policies, MFA, Encryption - Access Control: RBAC, DAC, Roles, Privileges - Trust Center, Data Lineage, Compliance |
| Topic 2: Data Loading, Unloading & Connectivity | 18% | - Stages, File Formats, COPY INTO, Snowpipe - Unloading Data, Export Options - External Integrations, Connectors, SnowSQL - Bulk Load, Streaming, Dynamic Tables |
| Topic 3: Data Collaboration & Protection | 10% | - Data Clean Rooms, Collaboration Features - Secure Data Sharing, Listings, Marketplace - Data Protection, Retention, Time Travel, Fail-safe |
| Topic 4: Performance Optimization, Querying & Transformation | 21% | - SQL Transformations, Semi-structured/Unstructured Data - Cortex AI, Notebooks, Apache Iceberg Tables - Query Processing, Profiling, Optimization - Materialized Views, Streams, Tasks, Pipelines |
| Topic 5: Snowflake Architecture & Core Concepts | 31% | - Virtual Warehouses, Clustering, Caching - Snowsight Interface, Core Platform Features - Cloud Services Layer, Compute Layer, Storage Layer - Databases, Schemas, Objects, Data Types |
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NEW QUESTION # 780
Which of the following are best practices for loading data into Snowflake? (Choose three.)
Answer: C,D,F
Explanation:
Best practices for loading data into Snowflake include aiming for data file sizes between 100 MB and 250 MB when compressed, as this size is optimal for parallel processing and minimizes overhead. Enclosing fields with delimiter characters in quotes ensures proper field recognition during the load process. Splitting large files into smaller ones allows for better distribution of the load across compute resources, enhancing performance and efficiency.
NEW QUESTION # 781
Which file function gives a user or application access to download unstructured data from a Snowflake stage?
Answer: D
Explanation:
The function that provides access to download unstructured data from a Snowflake stage is:
C . GET_PRESIGNED_URL: This function generates a presigned URL for a single file within a stage. The generated URL can be used to directly access or download the file without needing to go through Snowflake. This is particularly useful for unstructured data such as images, videos, or large text files, where direct access via a URL is needed outside of the Snowflake environment.
Example usage:
SELECT GET_PRESIGNED_URL('stage_name', 'file_path');
This function simplifies the process of securely sharing or accessing files stored in Snowflake stages with external systems or users.
References:
Snowflake Documentation: GET_PRESIGNED_URL Function at Snowflake Documentation
NEW QUESTION # 782
How does a Snowflake user extract the URL of a directory table on an external stage for further transformation?
Answer: D
Explanation:
To extract the URL of a directory table on an external stage for further transformation in Snowflake, the GET_ABSOLUTE_PATH function can be used. This function returns the full path of a file or directory within a specified stage, enabling users to dynamically construct URLs for accessing or processing data stored in external stages.
References:
Snowflake Documentation: Working with Stages
NEW QUESTION # 783
The following JSON is stored in a VARIANT column called src of the CAR_SALES table:
A user needs to extract the dealership information from the JSON.
How can this be accomplished?
Answer: D
Explanation:
In Snowflake, to extract a specific element from a JSON stored in a VARIANT column, the correct syntax is to use the dot notation. Therefore, the query select src.dealership from car_sales; will return the dealership information contained within each JSON object in the src column.
References: For a detailed explanation, please refer to the Snowflake documentation on querying semi- structured data.
NEW QUESTION # 784
When a column in a table is dropped, what is the impact on the table ' s micro-partitions?
Answer: A
Explanation:
The correct answer is D. The existing micro-partitions will not be changed or impacted .
Important correction:
The provided answer B is not correct.
Snowflake stores table data in immutable micro-partitions. Because micro-partitions are immutable, Snowflake does not physically rewrite all existing micro-partitions immediately when a column is dropped.
Instead, metadata is updated so that the dropped column is no longer visible or accessible in the table definition.
Why D is correct:
Dropping a column is a metadata operation. The underlying existing micro-partitions are not immediately rewritten to remove the column data. Snowflake's immutable micro-partition design avoids expensive full- table rewrites for this type of operation.
Why the other options are incorrect:
A). Snowflake does not immediately create new micro-partitions containing only the remaining columns.
B). Existing micro-partitions are immutable and are not updated in place.
C). Snowflake does not automatically create a new table and rewrite all remaining data when a column is dropped.
Official Snowflake documentation reference:
Snowflake documentation describes micro-partitions as immutable storage units. Table metadata operations, such as dropping columns, do not immediately rewrite existing micro-partitions.
Reference: Snowflake Documentation - Micro-partitions and data clustering; Snowflake Documentation - ALTER TABLE ... DROP COLUMN; SnowPro Core Study Guide - Snowflake Architecture.
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NEW QUESTION # 785
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