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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Databases, Tables & Views | 18% | - Table types (permanent, transient, temporary) - Stored procedures and UDFs - Column types and sequences - Data manipulation (DML operations) - Views (standard, materialized, secure) - Clustering and micro-partitions |
| Topic 2: Data Sharing | 12% | - Data listing marketplace - Reader account management - Private sharing vs. Data Exchange - Shares and reader accounts |
| Topic 3: Security & Access Control | 10% | - Authentication (SSO, MFA) - Masking policies - Row-level and column-level security - Authorization (roles, privileges) - Governance best practices |
| Topic 4: Virtual Warehouses | 15% | - Cache concepts (warehouse, result, metadata) - Warehouse creation and configuration - Warehouse sizing and scaling - Multi-cluster warehouses - Warehouse monitoring and troubleshooting |
| Topic 5: Performance Optimization & Monitoring | 4% | - Query history and monitoring - Query profiling and performance tuning - Resource optimization techniques |
| Topic 6: Account Objects | 8% | - Resource monitors - Organizations and accounts structure - Warehouses, databases, and schemas hierarchy |
| Topic 7: Time Travel & Cloning | 10% | - Fail-safe concepts - Zero-copy cloning - Time travel concepts and retention - UNDROP and historical data access |
| Topic 8: Semi-Structured Data | 10% | - VARIANT data type - Parsing and flattening semi-structured data - Array and object operations |
| Topic 9: Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake editions and features - Snowflake architecture layers (storage, compute, services) - Virtual warehouse concepts and credit usage |
| Topic 10: Data Loading & Unloading | 15% | - Data unloading and staging - Continuous loading with Snowpipe - Bulk loading with COPY command - File formats (JSON, CSV, Parquet, AVRO) - Data ingestion best practices |
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NEW QUESTION # 1118
Which command is used when loading data with Snowpipe?
Answer: D
Explanation:
The correct answer is B. COPY .
Snowpipe loads data using a pipe object. The pipe definition contains a COPY INTO < table > statement that tells Snowflake how to load staged files into a target table.
Why B is correct:
Snowpipe automates loading staged files, but the actual load operation is still based on the COPY INTO < table > command.
Example:
CREATE PIPE my_pipe
AS
COPY INTO my_table
FROM @my_stage
FILE_FORMAT = (TYPE = CSV);
Why the other options are incorrect:
A). INSERT adds rows manually or through query results, but Snowpipe does not use INSERT as its load command.
C). UPDATE modifies existing rows and is not used by Snowpipe for loading files.
D). MERGE performs insert/update/delete logic based on matching conditions, but Snowpipe uses COPY INTO < table > for file ingestion.
Official Snowflake documentation reference:
Snowflake documentation explains that a pipe defines a COPY INTO < table > statement used by Snowpipe to load data from staged files.
Reference: Snowflake Documentation - Snowpipe; Snowflake Documentation - CREATE PIPE; Snowflake Documentation - COPY INTO < table > ; SnowPro Core Study Guide - Data Loading and Unloading.
==
NEW QUESTION # 1119
What is required to set a stored procedure to EXECUTE AS? (Select TWO).
Answer: B,C
NEW QUESTION # 1120
Which Snowflake feature allows a user to track sensitive data for compliance, discovery, protection, and resource usage?
Answer: A
Explanation:
Tags in Snowflake allow users to track sensitive data for compliance, discovery, protection, and resource usage. They enable the categorization and tracking of data, supporting compliance with privacy regulations678. References: [ COF-C03 ] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 1121
A team plans to build data-transformation pipelines using a Snowflake-supported development framework that provides compute pushdown and native integration with Snowflake features.
Which feature should the team use?
Answer: C
Explanation:
Snowpark provides language-specific APIs for building data-processing and transformation logic. Snowpark operations are translated into SQL and executed by Snowflake's compute engine. This provides compute pushdown because processing occurs inside Snowflake instead of requiring the complete dataset to be transferred to an external application.
The JDBC and ODBC drivers provide database connectivity. They allow applications to submit SQL statements and retrieve results, but they are not transformation frameworks comparable to Snowpark.
The Snowflake Connector for Kafka is primarily used to ingest streaming data from Apache Kafka into Snowflake.
Correction: The original option A said "Snowflake Connector for Python." That connector is a Python database driver. The feature that provides DataFrame-based transformations and compute pushdown is the Snowpark API for Python.
References:
Snowflake Documentation - Snowpark Developer Guide for Python: https://docs.snowflake.com/en
/developer-guide/snowpark/python/index
Snowflake Documentation - Snowpark API overview: https://docs.snowflake.com/en/developer-guide
/snowpark/index
==
NEW QUESTION # 1122
Which features are included in Snowsight? (Select TWO)
Answer: B,C
NEW QUESTION # 1123
......
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