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| Section | Weight | Objectives |
|---|---|---|
| Virtual Warehouses | 15% | - Warehouse creation and configuration - Multi-cluster warehouses - Warehouse sizing and scaling - Cache concepts (warehouse, result, metadata) - Warehouse monitoring and troubleshooting |
| Time Travel & Cloning | 10% | - Time travel concepts and retention - Fail-safe concepts - Zero-copy cloning - UNDROP and historical data access |
| Data Sharing | 12% | - Data listing marketplace - Reader account management - Shares and reader accounts - Private sharing vs. Data Exchange |
| Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake editions and features - Virtual warehouse concepts and credit usage - Snowflake architecture layers (storage, compute, services) |
| Data Loading & Unloading | 15% | - Bulk loading with COPY command - Data ingestion best practices - Data unloading and staging - Continuous loading with Snowpipe - File formats (JSON, CSV, Parquet, AVRO) |
| Security & Access Control | 10% | - Governance best practices - Authentication (SSO, MFA) - Masking policies - Authorization (roles, privileges) - Row-level and column-level security |
| Databases, Tables & Views | 18% | - Data manipulation (DML operations) - Stored procedures and UDFs - Views (standard, materialized, secure) - Column types and sequences - Table types (permanent, transient, temporary) - Clustering and micro-partitions |
| Performance Optimization & Monitoring | 4% | - Query profiling and performance tuning - Resource optimization techniques - Query history and monitoring |
| Account Objects | 8% | - Organizations and accounts structure - Resource monitors - Warehouses, databases, and schemas hierarchy |
| Semi-Structured Data | 10% | - Array and object operations - Parsing and flattening semi-structured data - VARIANT data type |
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NEW QUESTION # 1169
What does the LATERAL modifier for the FLATTEN function do?
Answer: D
Explanation:
The LATERAL modifier for the FLATTEN function allows joining information outside the object (such as other columns in the source table) with the flattened data, creating a lateral view that correlates with the preceding tables in the FROM clause2345. References: [ COF-C03 ] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 1170
A user has a standard multi-cluster warehouse auto-scaling policy in place.
Which condition will trigger a cluster to shut-down?
Answer: D
Explanation:
In a standard multi-cluster warehouse with auto-scaling, a cluster will shut down when, after 2-3 consecutive checks, the system determines that the load on the least-loaded cluster could be redistributed to other clusters. This ensures efficient resource utilization and cost management. References: [COF-C02] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 1171
When a column in a table is dropped, what is the impact on the table ' s micro-partitions?
Answer: C
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.
==
NEW QUESTION # 1172
How are serverless features billed?
Answer: B
Explanation:
Serverless features in Snowflake are billed based on the time they are used, measured in minutes. The cost is calculated by multiplying the duration of the job by an automatic sizing determined by Snowflake, with a minimum billing increment of one minute. This means that even if a serverless feature is used for less than a minute, it will still be billed for the full minute.
NEW QUESTION # 1173
Which Snowflake tool would be BEST to troubleshoot network connectivity?
Answer: C
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 # 1174
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