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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Loading, Unloading & Connectivity | 18% | - Bulk Load, Streaming, Dynamic Tables - External Integrations, Connectors, SnowSQL - Unloading Data, Export Options - Stages, File Formats, COPY INTO, Snowpipe |
| Topic 2: Account Management & Data Governance | 20% | - Access Control: RBAC, DAC, Roles, Privileges - Security: Network Policies, MFA, Encryption - Governance: Resource Monitors, Data Masking, Row/Column Security - Trust Center, Data Lineage, Compliance |
| Topic 3: Snowflake Architecture & Core Concepts | 31% | - Databases, Schemas, Objects, Data Types - Snowsight Interface, Core Platform Features - Virtual Warehouses, Clustering, Caching - Cloud Services Layer, Compute Layer, Storage Layer |
| 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: Performance Optimization, Querying & Transformation | 21% | - Materialized Views, Streams, Tasks, Pipelines - Query Processing, Profiling, Optimization - SQL Transformations, Semi-structured/Unstructured Data - Cortex AI, Notebooks, Apache Iceberg Tables |
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NEW QUESTION # 648
What impacts the credit consumption of maintaining a materialized view? (Choose two.)
Answer: B,C
Explanation:
The credit consumption for maintaining a materialized view is impacted by how often the base table changes and whether the materialized view has a cluster key defined (D). Changes to the base table can trigger a refresh of the materialized view, consuming credits. Additionally, having a cluster key defined can optimize the performance and credit usage during the materialized view's maintenance.References:SnowPro Core Certification materialized view credit consumption
NEW QUESTION # 649
A size X-Small virtual warehouse ran for 90 seconds and was shut down. The warehouse was then run for another 30 seconds before being shut down again.
How many seconds will be billed?
Answer: D
NEW QUESTION # 650
Which Snowflake tool would be BEST to troubleshoot network connectivity?
Answer: A
Explanation:
SnowCD (Snowflake Connectivity Diagnostic Tool) is the best tool provided by Snowflake for troubleshooting network connectivity issues.It helps diagnose and resolve issues related to connecting to Snowflake services
https://docs.snowflake.com/en/user-guide/snowcd.html#:~:text=SnowCD%20(i.e.%20Snowflake%20Connectivity%20Diagnostic,their%20network%20connection%20to%20Snowflake.
NEW QUESTION # 651
What notation methods are supported in Snowflake to access elements in a JSON object? Select TWO.
Answer: C,D
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.
NEW QUESTION # 652
Which view can be used to determine if a table has frequent row updates or deletes?
Answer: B
Explanation:
The TABLE_STORAGE_METRICS view can be used to determine if a table has frequent row updates or deletes. This view provides detailed metrics on the storage utilization of tables within Snowflake, including metrics that reflect the impact of DML operations such as updates and deletes on table storage. For example, metrics related to the number of active and deleted rows can help identify tables that experience high levels of row modifications, indicating frequent updates or deletions.
References:
Snowflake Documentation: TABLE_STORAGE_METRICS View
NEW QUESTION # 653
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