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| Section | Weight | Objectives |
|---|---|---|
| Semi-Structured Data | 10% | - VARIANT data type - Array and object operations - Parsing and flattening semi-structured data |
| Time Travel & Cloning | 10% | - Fail-safe concepts - UNDROP and historical data access - Time travel concepts and retention - Zero-copy cloning |
| Cloud Platform Overview & Snowflake Key Concepts | 8% | - Virtual warehouse concepts and credit usage - Snowflake editions and features - Snowflake architecture layers (storage, compute, services) |
| Security & Access Control | 10% | - Masking policies - Authorization (roles, privileges) - Row-level and column-level security - Authentication (SSO, MFA) - Governance best practices |
| Performance Optimization & Monitoring | 4% | - Query history and monitoring - Query profiling and performance tuning - Resource optimization techniques |
| Data Loading & Unloading | 15% | - Data unloading and staging - Bulk loading with COPY command - File formats (JSON, CSV, Parquet, AVRO) - Continuous loading with Snowpipe - Data ingestion best practices |
| Data Sharing | 12% | - Shares and reader accounts - Private sharing vs. Data Exchange - Reader account management - Data listing marketplace |
| Databases, Tables & Views | 18% | - Clustering and micro-partitions - Data manipulation (DML operations) - Table types (permanent, transient, temporary) - Column types and sequences - Views (standard, materialized, secure) - Stored procedures and UDFs |
| Virtual Warehouses | 15% | - Warehouse sizing and scaling - Multi-cluster warehouses - Cache concepts (warehouse, result, metadata) - Warehouse monitoring and troubleshooting - Warehouse creation and configuration |
| Account Objects | 8% | - Resource monitors - Organizations and accounts structure - Warehouses, databases, and schemas hierarchy |
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NEW QUESTION # 1000
In Snowflake ' s data security framework, how does column-level security contribute to the protection of sensitive information? (Select TWO).
Answer: A,C
Explanation:
Column-level security in Snowflake enhances data protection by restricting access and applying masking policies to sensitive data at the column level.
Limiting Access Based on User Privileges:
Column-level security allows administrators to define which users or roles have access to specific columns within a table.
This ensures that sensitive data is only accessible to authorized personnel, thereby reducing the risk of data breaches.
Application of Masking Policies:
Masking policies can be applied to columns to obfuscate sensitive data.
For example, credit card numbers can be masked to show only the last four digits, protecting the full number from being exposed.
Snowflake Documentation: Column-Level Security
Snowflake Documentation: Dynamic Data Masking
NEW QUESTION # 1001
A view is defined on a permanent table. A temporary table with the same name is created in the same schema as the referenced table. What will the query from the view return?
Answer: D
Explanation:
When a view is defined on a permanent table, and a temporary table with the same name is created in the same schema, the query from the view will return the data from the permanent table.Temporary tables are session-specific and do not affect the data returned by views defined on permanent tables2.
NEW QUESTION # 1002
What Snowflake role must be granted for a user to create and manage accounts?
Answer: D
Explanation:
The ACCOUNTADMIN role is required for a user to create and manage accounts in Snowflake.This role has the highest level of privileges and is responsible for managing all aspects of the Snowflake account, including the ability to create and manage other user accounts1.
https://docs.snowflake.com/en/user-guide/security-access-control-considerations.html
NEW QUESTION # 1003
What are characteristics of the ownership privilege when it is granted on a regular Snowflake schema? (Select TWO).
Answer: C,E
Explanation:
In Snowflake, the ownership privilege for a schema includes:
Automatic granting to the creator's role: The role that creates a database object within the schema automatically receives ownership of that object.
Ability to manage grants: Ownership enables the role to manage permissions and grants on the schema and its objects, allowing them to control access at the schema level.
Ownership does not directly confer query privileges or the ability to alter warehouse settings, nor is it transferable without specific privilege management actions.
NEW QUESTION # 1004
Which TABLE function helps to convert semi-structured data to a relational representation?
Answer: D
Explanation:
The FLATTEN table function in Snowflake is used to convert semi-structured data, such as JSON or XML, into a relational format.It expands nested arrays or objects into multiple rows, making the data suitable for relational querying3.
NEW QUESTION # 1005
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