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| Section | Weight | Objectives |
|---|---|---|
| Performance Optimization, Querying & Transformation | 21% | - Materialized Views, Streams, Tasks, Pipelines - Query Processing, Profiling, Optimization - Cortex AI, Notebooks, Apache Iceberg Tables - SQL Transformations, Semi-structured/Unstructured Data |
| Snowflake Architecture & Core Concepts | 31% | - Cloud Services Layer, Compute Layer, Storage Layer - Databases, Schemas, Objects, Data Types - Snowsight Interface, Core Platform Features - Virtual Warehouses, Clustering, Caching |
| Account Management & Data Governance | 20% | - Access Control: RBAC, DAC, Roles, Privileges - Security: Network Policies, MFA, Encryption - Trust Center, Data Lineage, Compliance - Governance: Resource Monitors, Data Masking, Row/Column Security |
| Data Loading, Unloading & Connectivity | 18% | - Unloading Data, Export Options - Stages, File Formats, COPY INTO, Snowpipe - External Integrations, Connectors, SnowSQL - Bulk Load, Streaming, Dynamic Tables |
| Data Collaboration & Protection | 10% | - Data Clean Rooms, Collaboration Features - Data Protection, Retention, Time Travel, Fail-safe - Secure Data Sharing, Listings, Marketplace |
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質問 # 443
What is the data lineage feature used for in Snowflake?
正解：B
解説：
The correct answer is C. To track how data has been transformed among columns .
Snowflake's data lineage capability is used to understand how data flows and changes across objects. It helps users see relationships between source and target objects and understand how data is transformed, especially at the column level.
Corrected wording:
The original option said "records," but Snowflake data lineage is more accurately described as tracking how data is transformed among columns, not individual row-level record transformations.
Why C is correct:
Data lineage helps track how data moves from source columns to target columns through operations such as views, CTAS statements, INSERT statements, and other transformations. This is useful for governance, impact analysis, auditing, and understanding downstream dependencies.
Why the other options are incorrect:
A). Tracking how records are read by a user is more closely related to access history or query history, not data lineage.
B). Tracking dependencies at the object level is related to object dependencies. Data lineage is more specific because it can show how data flows between columns and objects.
D). Snowflake data lineage is not primarily a row-level feature for tracking the impact of transformations on individual records.
Official Snowflake documentation reference:
Snowflake documentation describes data lineage as a governance capability that helps track how data moves and transforms across Snowflake objects, including column-level relationships.
Reference: Snowflake Documentation - Data lineage; Snowflake Documentation - Object dependencies; SnowPro Core Study Guide - Data Protection and Governance.
質問 # 444
Which query profile statistics help determine if efficient pruning is occurring? (Choose two.)
正解：B、E
解説：
Efficient pruning in Snowflake is indicated by the number of partitions scanned out of the total available.If a small percentage of partitions are scanned, it suggests that the pruning process is effectively narrowing down the data, which can lead to improved query performance
質問 # 445
Which table function is used to perform additional processing on the results of a previously-run query?
正解：C
解説：
TheRESULT_SCANtable function is used in Snowflake to perform additional processing on the results of a previously-run query. It allows users to reference the result set of a previous query by its query ID, enabling further analysis or transformations without re-executing the original query.
Snowflake Documentation: RESULT_SCAN
質問 # 446
What are value types that a VARIANT column can store? (Select TWO)
正解：A、D
解説：
A VARIANT column in Snowflake can store semi-structured data types. This includes:
B . OBJECT: An object is a collection of key-value pairs in JSON, and a VARIANT column can store this type of data structure.
D . ARRAY: An array is an ordered list of zero or more values, which can be of any variant-supported data type, including objects or other arrays.
The VARIANT data type is specifically designed to handle semi-structured data like JSON, Avro, ORC, Parquet, or XML, allowing for the storage of nested and complex data structures.
References:
Snowflake Documentation on Semi-Structured Data Types
SnowPro Core Certification Study Guide
質問 # 447
Which of the following compute resources or features are managed by Snowflake? (Select TWO).
正解：B、E
解説：
Snowflake manages various compute resources and features, including Snowpipe and the ability to scale up a warehouse. Snowpipe is Snowflake's continuous data ingestion service that allows users to load data as soon as it becomes available. Scaling up a warehouse refers to increasing the compute resources allocated to a virtual warehouse to handle larger workloads or improve performance.
References:
[COF-C02] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Snowpipe and Virtual Warehouses1
質問 # 448
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