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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 |
| Performance Optimization & Monitoring | 4% | - Query history and monitoring - Resource optimization techniques - Query profiling and performance tuning |
| Account Objects | 8% | - Organizations and accounts structure - Warehouses, databases, and schemas hierarchy - Resource monitors |
| Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake architecture layers (storage, compute, services) - Virtual warehouse concepts and credit usage - Snowflake editions and features |
| Semi-Structured Data | 10% | - Array and object operations - Parsing and flattening semi-structured data - VARIANT data type |
| Time Travel & Cloning | 10% | - Time travel concepts and retention - Fail-safe concepts - UNDROP and historical data access - Zero-copy cloning |
| Databases, Tables & Views | 18% | - Stored procedures and UDFs - Views (standard, materialized, secure) - Table types (permanent, transient, temporary) - Clustering and micro-partitions - Column types and sequences - Data manipulation (DML operations) |
| Data Sharing | 12% | - Private sharing vs. Data Exchange - Shares and reader accounts - Data listing marketplace - Reader account management |
| Security & Access Control | 10% | - Governance best practices - Masking policies - Authentication (SSO, MFA) - Row-level and column-level security - Authorization (roles, privileges) |
| Data Loading & Unloading | 15% | - File formats (JSON, CSV, Parquet, AVRO) - Bulk loading with COPY command - Data unloading and staging - Data ingestion best practices - Continuous loading with Snowpipe |
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問題 #891
Which Snowflake table objects can be shared with other accounts? (Select TWO).
答案：B,C
解題說明：
In Snowflake,permanent tables and external tables can be shared with other accounts using Secure Data Sharing. Temporary tables, transient tables, and UDTFs are not shareable objects
問題 #892
A Snowflake user creates this stored procedure:
PROCEDURE SALES_REPORT(MONTH INT, YEAR INT);
Which statement will remove the stored procedure?
答案：B
解題說明：
The correct answer is B. DROP PROCEDURE SALES_REPORT(INT, INT); .
In Snowflake, stored procedures can be overloaded. This means multiple procedures can have the same name but different argument signatures. Because of this, when dropping a stored procedure, the procedure name and argument data types are used to identify the exact procedure to remove.
Why B is correct:
The procedure was created with two arguments:
MONTH INT, YEAR INT
When dropping the procedure, Snowflake uses the argument data types, not the argument names. Therefore, the correct statement is:
DROP PROCEDURE SALES_REPORT(INT, INT);
Why the other options are incorrect:
A). The argument names are not included in the drop signature. Snowflake expects the argument data types.
C). This includes only argument names without data types, which is not the correct procedure signature syntax.
D). This does not identify the procedure signature and is not sufficient when dropping a stored procedure.
Official Snowflake documentation reference:
Snowflake documentation for DROP PROCEDURE explains that the procedure name and argument data types are required to identify the stored procedure to drop.
Reference: Snowflake Documentation - DROP PROCEDURE; Snowflake Documentation - Stored procedures; SnowPro Core Study Guide - SQL and Snowflake Objects.
問題 #893
What are the results of the NULL_COUNT Data Metric Function, or DMF, used to verify?
答案：A
解題說明：
The correct answer is D. The volume of the data .
The NULL_COUNT Data Metric Function returns the number of NULL values in a column. This type of metric is used to verify data quality by measuring the amount, or volume, of missing values.
Why D is correct:
NULL_COUNT counts how many rows contain NULL in a specified column. Since it produces a count, it is used to evaluate the volume of missing data.
Example concept:
SELECT SNOWFLAKE.CORE.NULL_COUNT(
SELECT column_name FROM my_table
);
This returns the number of NULL values found in the selected column.
Why the other options are incorrect:
A). Accuracy verifies whether data values are correct, but NULL_COUNT only counts missing values.
B). Uniqueness is verified with metrics that check duplicate or distinct values, not NULL_COUNT.
C). Freshness measures how current or recent the data is, not how many nulls exist.
Official Snowflake documentation reference:
Snowflake documentation describes NULL_COUNT as a system Data Metric Function that returns the number of NULL values in a column.
Reference: Snowflake Documentation - Data Metric Functions; Snowflake Documentation - SNOWFLAKE.CORE.NULL_COUNT; SnowPro Core Study Guide - Data Protection and Governance.
問題 #894
Which commands can only be executed using SnowSQL? (Select TWO).
答案：A,D
解題說明：
The LIST and PUT commands are specific to SnowSQL and cannot be executed in the web interface or other SQL clients.LIST is used to display the contents of a stage, and PUTis used to upload files to a stage. References: [COF-C02] SnowPro Core Certification Exam Study Guide
問題 #895
Which Snowflake view is used to support compliance auditing?
答案：A
解題說明：
The ACCESS_HISTORY view in Snowflake is utilized to support compliance auditing. It provides detailed information on data access within Snowflake, including reads and writes by user queries. This view is essential for regulatory compliance auditing as it offers insights into the usage of tables and columns, and maintains a direct link between the user, the query, and the accessed data1.
References: [ COF-C03 ] SnowPro Core Certification Exam Study Guide
問題 #896
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