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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Databases, Tables & Views | 18% | - Clustering and micro-partitions - Stored procedures and UDFs - Views (standard, materialized, secure) - Data manipulation (DML operations) - Table types (permanent, transient, temporary) - Column types and sequences |
| Topic 2: Time Travel & Cloning | 10% | - Time travel concepts and retention - Zero-copy cloning - Fail-safe concepts - UNDROP and historical data access |
| Topic 3: Cloud Platform Overview & Snowflake Key Concepts | 8% | - Virtual warehouse concepts and credit usage - Snowflake editions and features - Snowflake architecture layers (storage, compute, services) |
| Topic 4: Semi-Structured Data | 10% | - Array and object operations - VARIANT data type - Parsing and flattening semi-structured data |
| Topic 5: Security & Access Control | 10% | - Governance best practices - Masking policies - Authorization (roles, privileges) - Authentication (SSO, MFA) - Row-level and column-level security |
| Topic 6: Data Loading & Unloading | 15% | - Continuous loading with Snowpipe - Data ingestion best practices - Data unloading and staging - File formats (JSON, CSV, Parquet, AVRO) - Bulk loading with COPY command |
| Topic 7: Data Sharing | 12% | - Data listing marketplace - Reader account management - Private sharing vs. Data Exchange - Shares and reader accounts |
| Topic 8: Account Objects | 8% | - Warehouses, databases, and schemas hierarchy - Organizations and accounts structure - Resource monitors |
| Topic 9: Performance Optimization & Monitoring | 4% | - Query history and monitoring - Resource optimization techniques - Query profiling and performance tuning |
| Topic 10: Virtual Warehouses | 15% | - Warehouse creation and configuration - Multi-cluster warehouses - Cache concepts (warehouse, result, metadata) - Warehouse monitoring and troubleshooting - Warehouse sizing and scaling |
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NEW QUESTION # 68
Which Snowflake feature records changes mace to a table so actions can be taken using that change data capture?
Answer: A
Explanation:
Snowflake ' s Streams feature is specifically designed for change data capture (CDC). A stream records insert, update, and delete operations performed on a table, and allows users to query these changes. This enables actions to be taken on the changed data, facilitating processes like incremental data loads and real-time analytics. Streams provide a powerful mechanism for applications to respond to data changes in Snowflake tables efficiently.
References: Snowflake Documentation on Streams
NEW QUESTION # 69
What happens when a suspended virtual warehouse is resized in Snowflake?
Answer: B
Explanation:
In Snowflake, resizing a virtual warehouse that is currently suspended does not immediately provision the new compute resources. Instead, the change in size is recorded, and the additional compute resources are provisioned when the warehouse is resumed. This means that the action of resizing a suspended warehouse does not cause it to resume operation automatically. The warehouse remains suspended until an explicit command to resume it is issued, or until it automatically resumes upon the next query execution that requires it.
This behavior allows for efficient management of compute resources, ensuring that credits are not consumed by a warehouse that is not in use, even if its size is adjusted while it is suspended.
Reference: Snowflake Documentation on Resizing Warehouses (https://docs.snowflake.com/en/user-guide
/warehouses-tasks.html#resizing-a-warehouse)
NEW QUESTION # 70
What command is used to unload data from a Snowflake table to a local file system?
Answer: B
Explanation:
The GET command is used to download (i.e., unload) files from a Snowflake internal stage to the local file system . COPY INTO writes to stages, not directly to local disk.
Reference:
Snowflake Docs: GET Command
NEW QUESTION # 71
A user needs to set up a virtual warehouse for the organization's BI team. The team has around 14 users running similar queries at the same time, and queries often get queued. Which virtual warehouse configuration should be assigned to the team to improve performance and concurrency?
Answer: C
Explanation:
Queued queries from many concurrent BI users indicate a concurrency issue. A multi-cluster warehouse improves concurrency by adding clusters as query demand increases. Increasing warehouse size helps individual query speed, but adding clusters is better for concurrent query queuing.
Reference: Snowflake Documentation - Multi-cluster warehouses; Warehouse load and concurrency.
NEW QUESTION # 72
What is the recommended compressed file size range for continuous data loads using Snowpipe?
Answer: B
Explanation:
For continuous data loads using Snowpipe, the recommended compressed file size range is between 100-250 MB.This size range is suggested to optimize the number of parallel operations for a load and to avoid size limitations, ensuring efficient and cost-effective data loading
NEW QUESTION # 73
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