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| Section | Weight | Objectives |
|---|---|---|
| Account Objects | 8% | - Organizations and accounts structure - Warehouses, databases, and schemas hierarchy - Resource monitors |
| Semi-Structured Data | 10% | - VARIANT data type - Array and object operations - Parsing and flattening semi-structured data |
| Security & Access Control | 10% | - Authorization (roles, privileges) - Authentication (SSO, MFA) - Governance best practices - Masking policies - Row-level and column-level security |
| Performance Optimization & Monitoring | 4% | - Query profiling and performance tuning - Query history and monitoring - Resource optimization techniques |
| Databases, Tables & Views | 18% | - Views (standard, materialized, secure) - Data manipulation (DML operations) - Table types (permanent, transient, temporary) - Clustering and micro-partitions - Stored procedures and UDFs - Column types and sequences |
| Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake architecture layers (storage, compute, services) - Virtual warehouse concepts and credit usage - Snowflake editions and features |
| Data Sharing | 12% | - Shares and reader accounts - Reader account management - Private sharing vs. Data Exchange - Data listing marketplace |
| Data Loading & Unloading | 15% | - File formats (JSON, CSV, Parquet, AVRO) - Data ingestion best practices - Bulk loading with COPY command - Data unloading and staging - Continuous loading with Snowpipe |
| Time Travel & Cloning | 10% | - UNDROP and historical data access - Zero-copy cloning - Time travel concepts and retention - Fail-safe concepts |
| Virtual Warehouses | 15% | - Multi-cluster warehouses - Cache concepts (warehouse, result, metadata) - Warehouse monitoring and troubleshooting - Warehouse creation and configuration - Warehouse sizing and scaling |
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NEW QUESTION # 865
Which Snowflake feature can be used to find sensitive data in a table or column?
Answer: B
Explanation:
Data classification in Snowflake is a feature that allows organizations to identify and categorize data stored in tables or columns based on its sensitivity level or content type. This feature can be used to find sensitive data within the database by classifying data as confidential, personal, public, etc., making it easier to apply appropriate security measures, such as masking policies or row-level security, to protect sensitive information.
References:
Snowflake Documentation: Data Classification
NEW QUESTION # 866
How can a user MINIMIZE Continuous Data Protection costs when using large, high-churn, dimension tables?
Answer: A
Explanation:
To minimize Continuous Data Protection (CDP) costs when dealing with large, high-churn dimension tables in Snowflake, using transient tables is a recommended approach.
Transient Tables: These are designed for data that does not require fail-safe protection. They provide the benefit of reducing costs associated with continuous data protection since they do not have the seven-day Fail- safe period that is mandatory for permanent tables.
Periodic Copying to Permanent Tables: By periodically copying data from transient tables to permanent tables, you can achieve a balance between data protection and cost-efficiency. Permanent tables offer the extended data protection features, including Time Travel and Fail-safe, butthese features can be applied selectively rather than continuously, reducing the overall CDP costs.
Snowflake Documentation on Transient Tables
Snowflake Documentation on Time Travel & Fail-safe
NEW QUESTION # 867
Assuming the data provider has granted the required permissions to the objects in a share, which statement will grant permissions to the role consumer_role, allowing access to the database share_db?
Answer: A
NEW QUESTION # 868
What is the MINIMUM size requirement when creating a Snowpark-optimized virtual warehouse?
Answer: D
Explanation:
When creating a Snowpark-optimized virtual warehouse in Snowflake, the minimum size requirement is Small. Snowpark is designed to handle data processing workloads efficiently, and the Small size ensures adequate resources for such tasks.
Virtual Warehouse Sizes:
Snowflake offers different sizes for virtual warehouses, ranging from X-Small to 6X-Large.
Each size corresponds to a specific level of compute resources.
Minimum Size Requirement for Snowpark:
A Small virtual warehouse is the minimum size required to optimize performance and resource allocation for Snowpark workloads.
This ensures that the warehouse has sufficient capacity to handle data processing and transformation tasks efficiently.
References:
Snowflake Documentation: Virtual Warehouse Sizes
NEW QUESTION # 869
Which command should be used to unload all the rows from a table into one or more files in a named stage?
Answer: B
Explanation:
To unload data from a table into one or more files in a named stage, the COPY INTO <location> command should be used. This command exports the result of a query, such as selecting all rows from a table, into files stored in the specified stage. The COPY INTO command is versatile, supporting various file formats and compression options for efficient data unloading.
References:
Snowflake Documentation: COPY INTO Location
NEW QUESTION # 870
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