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| Section | Weight | Objectives |
|---|---|---|
| Data Sharing | 12% | - Private sharing vs. Data Exchange - Data listing marketplace - Shares and reader accounts - Reader account management |
| Account Objects | 8% | - Resource monitors - Warehouses, databases, and schemas hierarchy - Organizations and accounts structure |
| Performance Optimization & Monitoring | 4% | - Resource optimization techniques - Query profiling and performance tuning - Query history and monitoring |
| Data Loading & Unloading | 15% | - Data unloading and staging - Bulk loading with COPY command - File formats (JSON, CSV, Parquet, AVRO) - Data ingestion best practices - Continuous loading with Snowpipe |
| Virtual Warehouses | 15% | - Warehouse creation and configuration - Warehouse sizing and scaling - Warehouse monitoring and troubleshooting - Cache concepts (warehouse, result, metadata) - Multi-cluster warehouses |
| Security & Access Control | 10% | - Row-level and column-level security - Masking policies - Authentication (SSO, MFA) - Governance best practices - Authorization (roles, privileges) |
| Semi-Structured Data | 10% | - Array and object operations - Parsing and flattening semi-structured data - VARIANT data type |
| Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake editions and features - Virtual warehouse concepts and credit usage - Snowflake architecture layers (storage, compute, services) |
| Databases, Tables & Views | 18% | - Views (standard, materialized, secure) - Clustering and micro-partitions - Column types and sequences - Stored procedures and UDFs - Data manipulation (DML operations) - Table types (permanent, transient, temporary) |
| Time Travel & Cloning | 10% | - Zero-copy cloning - UNDROP and historical data access - Fail-safe concepts - Time travel concepts and retention |
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NEW QUESTION # 133
What technique does Snowflake use to limit the number of micro-partitions scanned by each query?
Answer: A
Explanation:
Snowflake uses a technique called pruning to limit the number of micro-partitions scanned by each query.Pruning effectively filters out unnecessary micro-partitions based on the query's filter conditions, which can significantly improve query performance by reducing the amount of data scanned1.
NEW QUESTION # 134
Which Snowsight feature can be used to perform data manipulations and transformations using a programming language?
Answer: D
Explanation:
Python worksheets in Snowsight enable users to perform data manipulations and transformations using the Python programming language directly within the Snowflake environment. This feature integrates the power of Python with Snowflake ' s data warehousing capabilities, allowing for sophisticated data analysis and manipulation.
Introduction to Python Worksheets:
Python worksheets provide an interactive environment to write and execute Python code.
They are designed to facilitate data science and data engineering tasks.
Functionality:
Users can run Python scripts to manipulate data stored in Snowflake.
It allows for leveraging Python ' s extensive libraries for data analysis, machine learning, and more.
Integration with Snowflake:
Python worksheets run on Snowflake ' s compute infrastructure, ensuring scalability and performance.
They can access and manipulate Snowflake tables directly, making them a powerful tool for data transformation.
Snowflake Documentation: Snowsight Python Worksheets
NEW QUESTION # 135
User-level network policies can be created by which of the following roles? (Select TWO).
Answer: B,D
Explanation:
User-level network policies in Snowflake can be created by roles with the necessary privileges to manage security and account settings. The ACCOUNTADMIN role has the highest level of privileges across the account, including the ability to manage network policies. The SECURITYADMIN role is specifically responsible for managing security objects within Snowflake, which includes the creation and management of network policies.
References:
[ COF-C03 ] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Network Policies1
Section 1.3 - SnowPro Core Certification Study Guide
NEW QUESTION # 136
What persistent data structures are used by the search optimization service to improve the performance of point lookups?
Answer: C
Explanation:
The search optimization service in Snowflake uses persistent data structures known as search access paths to improve the performance of point lookups. These structures enable efficient retrieval of data by reducing the amount of data scanned during queries.
Search Access Paths:
Search access paths are special indexing structures maintained by the search optimization service.
They store metadata about the distribution of data within tables, enabling faster lookups for specific values.
Point Lookups:
Point lookups involve searching for a specific value within a column.
By leveraging search access paths, Snowflake can quickly locate the exact micro-partition containing the value, minimizing the amount of data scanned.
Performance Improvement:
The use of search access paths significantly reduces query execution time for point lookups.
This is especially beneficial for large tables where scanning all micro-partitions would be computationally expensive.
References:
Snowflake Documentation: Search Optimization Service
Snowflake Documentation: Understanding Search Access Paths
NEW QUESTION # 137
Which Snowflake feature is used for both querying and restoring data?
Answer: A
Explanation:
Snowflake's Time Travel feature is used for both querying historical data in tables and restoring and cloning historical data in databases, schemas, and tables3. It allows users to access historical data within a defined period (1 day by default, up to 90 days for Snowflake Enterprise Edition) and is a key feature for data recovery and management.References:[COF-C02] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 138
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