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| Section | Weight | Objectives |
|---|---|---|
| Data Loading, Unloading & Connectivity | 18% | - Stages, File Formats, COPY INTO, Snowpipe - External Integrations, Connectors, SnowSQL - Bulk Load, Streaming, Dynamic Tables - Unloading Data, Export Options |
| Account Management & Data Governance | 20% | - Governance: Resource Monitors, Data Masking, Row/Column Security - Trust Center, Data Lineage, Compliance - Access Control: RBAC, DAC, Roles, Privileges - Security: Network Policies, MFA, Encryption |
| 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% | - Materialized Views, Streams, Tasks, Pipelines - Cortex AI, Notebooks, Apache Iceberg Tables - SQL Transformations, Semi-structured/Unstructured Data - Query Processing, Profiling, Optimization |
| Snowflake Architecture & Core Concepts | 31% | - Cloud Services Layer, Compute Layer, Storage Layer - Virtual Warehouses, Clustering, Caching - Snowsight Interface, Core Platform Features - Databases, Schemas, Objects, Data Types |
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NEW QUESTION # 688
What notation methods are supported in Snowflake to access elements in a JSON object? Select TWO.
Answer: A,E
Explanation:
The correct answers are C. Bracket notation and E. Dot notation .
Snowflake supports querying semi-structured data stored in columns such as VARIANT, OBJECT, and ARRAY. When working with JSON objects, users can access elements using either dot notation or bracket notation .
Why C is correct:
Bracket notation is used to access JSON object elements by enclosing the key name in brackets.
Example:
SELECT src[ ' customer ' ][ ' name ' ]
FROM json_table;
Bracket notation is especially useful when JSON keys contain special characters, spaces, or case-sensitive names.
Why E is correct:
Dot notation is used to access JSON object elements using a period between object levels.
Example:
SELECT src:customer.name
FROM json_table;
This notation is commonly used for simple JSON key paths.
Why the other options are incorrect:
A). CIDR notation is used for IP address ranges, not JSON element access.
B). Vertical bar notation is not a Snowflake method for accessing JSON object elements.
D). Slash notation is not a Snowflake method for accessing JSON object elements.
Official Snowflake documentation reference:
Snowflake documentation explains that semi-structured data elements in VARIANT columns can be accessed using dot notation and bracket notation.
Reference: Snowflake Documentation - Querying semi-structured data; Snowflake Documentation - Dot notation and bracket notation; SnowPro Core Study Guide - Working with Semi-Structured Data.
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NEW QUESTION # 689
What happens when a cloned table is replicated to a secondary database? (Select TWO)
Answer: B,E
Explanation:
When a cloned table is replicated to a secondary database in Snowflake, the following occurs:
C . The physical data is replicated: The actual data of the cloned table is physically replicated to the secondary database.This ensures that the secondary database has its own copy of the data, which can be used for read-only purposes or failover scenarios1.
E . Metadata pointers to cloned tables are replicated: Along with the physical data, the metadata pointers that refer to the cloned tables are also replicated.This metadata includes information about the structure of the table and any associated properties2.
It's important to note that while the physical data and metadata are replicated, the secondary database is typically read-only and cannot be used for write operations. Additionally, while there may be additional storage costs associated with the secondary account, this is not a direct result of the replication process but rather a consequence of storing additional data.
References:
SnowPro Core Exam Prep - Answers to Snowflake's LEVEL UP: Backup and Recovery Snowflake SnowPro Core Certification Exam Questions Set 10
NEW QUESTION # 690
Which service is designed to process unstructured data and put it into tables?
Answer: D
Explanation:
The correct answer is D. Document AI .
Document AI is a Snowflake Cortex feature designed to extract information from unstructured documents and make that information usable in structured form, such as rows and columns in Snowflake tables.
Why D is correct:
Document AI processes documents such as PDFs or forms and extracts values from them. The extracted data can then be stored in Snowflake tables for querying, analytics, and downstream processing.
Why the other options are incorrect:
A). Cortex Analyst enables natural language questions over structured data using semantic models. It is not the primary service for extracting data from documents into tables.
B). Cortex Search provides search over text and unstructured/semi-structured data, but it is not the main service for document extraction into tables.
C). Snowflake Copilot assists users with SQL and Snowflake workflows, but it is not the service specifically designed to process documents into table data.
Official Snowflake documentation reference:
Snowflake documentation describes Document AI as a Snowflake Cortex feature that uses AI to extract data from documents and process unstructured data for use in Snowflake.
Reference: Snowflake Documentation - Document AI; Snowflake Documentation - Snowflake Cortex; SnowPro Core Study Guide - Working with Semi-Structured Data and Snowflake Architecture.
NEW QUESTION # 691
Which statement accurately describes how a virtual warehouse functions?
Answer: C
Explanation:
A virtual warehouse in Snowflake is an independent compute cluster that performs data processing tasks such as executing SQL queries. Each virtual warehouse is dynamically allocated by Snowflake from the cloud provider's resources and does not share compute resources with other warehouses. This architecture ensures that the performance of one warehouse does not impact the performance of another. Adjusting the size of a virtual warehouse affects its computational power by increasing or decreasing the number of compute nodes, which can improve the performance of data processing tasks depending on the workload.
References:
Snowflake Documentation: Understanding Virtual Warehouses
NEW QUESTION # 692
What are the recommended alternative data types in Snowflake for unsupported large object data types such as BLOB and CLOB? (Select TWO).
Answer: A,D
NEW QUESTION # 693
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