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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Databases, Tables & Views | 18% | - Data manipulation (DML operations) - Column types and sequences - Clustering and micro-partitions - Stored procedures and UDFs - Table types (permanent, transient, temporary) - Views (standard, materialized, secure) |
| Topic 2: Performance Optimization & Monitoring | 4% | - Query profiling and performance tuning - Query history and monitoring - Resource optimization techniques |
| Topic 3: Data Loading & Unloading | 15% | - Data ingestion best practices - File formats (JSON, CSV, Parquet, AVRO) - Data unloading and staging - Continuous loading with Snowpipe - Bulk loading with COPY command |
| Topic 4: Data Sharing | 12% | - Reader account management - Shares and reader accounts - Data listing marketplace - Private sharing vs. Data Exchange |
| Topic 5: Cloud Platform Overview & Snowflake Key Concepts | 8% | - Virtual warehouse concepts and credit usage - Snowflake editions and features - Snowflake architecture layers (storage, compute, services) |
| Topic 6: Semi-Structured Data | 10% | - VARIANT data type - Parsing and flattening semi-structured data - Array and object operations |
| Topic 7: Account Objects | 8% | - Organizations and accounts structure - Warehouses, databases, and schemas hierarchy - Resource monitors |
| Topic 8: Security & Access Control | 10% | - Governance best practices - Row-level and column-level security - Masking policies - Authentication (SSO, MFA) - Authorization (roles, privileges) |
| Topic 9: Time Travel & Cloning | 10% | - Time travel concepts and retention - Zero-copy cloning - Fail-safe concepts - UNDROP and historical data access |
| Topic 10: Virtual Warehouses | 15% | - Warehouse monitoring and troubleshooting - Warehouse sizing and scaling - Cache concepts (warehouse, result, metadata) - Multi-cluster warehouses - Warehouse creation and configuration |
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NEW QUESTION # 252
Which command is used to unload files from an internal or external stage to a local file system?
Answer: C
Explanation:
The command used to unload files from an internal or external stage to a local file system in Snowflake is the GET command.This command allows users to download data files that have been staged, making them available on the local file system for further use23.
NEW QUESTION # 253
What feature specifies which roles are allowed to see a particular record in a table?
Answer: A
Explanation:
The correct answer is C. Row access policy .
A row access policy is used to implement row-level security in Snowflake. It controls whether a row is visible in query results based on conditions such as the current role, current user, or values in a mapping table.
Why C is correct:
The question asks which feature controls visibility of particular records, meaning rows. Row access policies are designed exactly for that purpose.
Example concept:
CREATE ROW ACCESS POLICY region_policy
AS (region STRING) RETURNS BOOLEAN - >
region IN (
SELECT allowed_region
FROM role_region_mapping
WHERE role_name = CURRENT_ROLE()
);
This policy can allow users with different roles to see only the rows assigned to their region.
Why the other options are incorrect:
A). Dynamic Data Masking controls how column values are displayed. It masks data in columns but does not filter entire rows.
B). Secure Data Sharing allows data to be shared between accounts, but it is not the row-level filtering feature itself.
D). Object tagging classifies or labels objects for governance, but it does not directly control row visibility.
Official Snowflake documentation reference:
Snowflake documentation describes row access policies as schema-level objects that determine whether a row is visible in query results.
Reference: Snowflake Documentation - Row access policies; Snowflake Documentation - Row-level security; SnowPro Core Study Guide - Data Protection and Governance.
==
NEW QUESTION # 254
What is a characteristic of materialized views in Snowflake?
Answer: B
Explanation:
One of the characteristics of materialized views in Snowflake is that they allow multiple tables to be joined in the underlying query.This enables the pre-computation of complex queries involving joins, which can significantly improve the performance of subsequent queries that access the materialized view4.References:[COF-C02] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 255
While running a query on a virtual warehouse in auto-scale mode, additional clusters are stated immediately if which setting is configured?
Answer: C
NEW QUESTION # 256
Which data types are supported by Snowflake when using semi-structured data? (Choose two.)
Answer: A,E
Explanation:
Snowflake supports the VARIANT and ARRAY data types for semi-structured data. VARIANT can store values of any other type, including OBJECT and ARRAY, making it suitable for semi-structured data formats like JSON.ARRAY is used to store an ordered list of elements
NEW QUESTION # 257
......
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