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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Protection and Sharing | 10% | - Reader accounts and data clean rooms - Secure data sharing - Snowflake Marketplace - Data retention and recovery |
| Topic 2: Data Transformations | 20% | - SQL and DDL/DML operations - Cloning, Time Travel, and Fail-safe - Dynamic tables and data pipelines - Working with semi-structured data |
| Topic 3: Account Access and Security | 20% | - Role-Based Access Control (RBAC) - Authentication and network policies - Privileges, grants, and ownership - Encryption and data protection - Account management and monitoring |
| Topic 4: Snowflake AI Data Cloud Architecture & Features | 25% | - Snowflake editions and cloud platform support - Three-layer architecture (Storage, Compute, Cloud Services) - Structured, semi-structured, and unstructured data support - Virtual warehouses and scaling - Micro-partitions, clustering, and caching |
| Topic 5: Data Loading and Unloading | 10% | - Continuous loading (Snowpipe) - Bulk loading methods - Data unloading and export - File formats and stages |
| Topic 6: Performance Concepts | 15% | - Query optimization and profiling - Search optimization service - Cost management and monitoring - Warehouse sizing and concurrency |
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NEW QUESTION # 71
True or False: A Virtual Warehouse can be resized while suspended.
Answer: A
Explanation:
Reference:
https://docs.snowflake.com/en/user-guide/warehouses-tasks.html#effects-of-resizing-a-suspended-warehouse
NEW QUESTION # 72
What can a user win a reader account do in Snowflake?
Answer: D
Explanation:
In Snowflake, a user within a reader account primarily has read-only access to the shared data. Reader accounts are created to enable third parties or separate business units to access and query data shared with them without allowing them to modify the underlying data. This means a user with a reader account can perform operations like querying shared data to analyze or report on it but cannot load new data, update existing data, or create new shares. This setup is crucial for maintaining data governance and security while enabling data sharing and collaboration.References: Snowflake Documentation on Reader Accounts
NEW QUESTION # 73
True or False: When Snowflake is configured to use Single Sign-on (sso), Snowflake receive the usernames and credentials from the sso service and loads them into the customer's Snowflake account.
Answer: A
Explanation:
Reference: https://docs.snowflake.com/en/user-guide/admin-security-fed-auth-configure-snowflake.html
NEW QUESTION # 74
Which virtual warehouse consideration can help lower compute resource credit consumption?
Answer: A
Explanation:
One key strategy to lower compute resource credit consumption in Snowflake is by automating the suspension and resumption of virtual warehouses. Virtual warehouses consume credits when they are running, and managing their operational times effectively can lead to significant cost savings.
A: Setting up a multi-cluster virtual warehouse increases parallelism and throughput but does not directly lower credit consumption. It is more about performance scaling than cost efficiency.
B: Resizing the virtual warehouse to a larger size increases the compute resources available for processing queries, which increases the credit consumption rate. This option does not help in lowering costs.
C: Automating the virtual warehouse suspension and resumption settings: This is a direct method to manage credit consumption efficiently. By automatically suspending a warehouse when it is not in use and resuming it when needed, you can avoid consuming credits during periods of inactivity. Snowflake allows warehouses to be configured to automatically suspend after a specified period of inactivity and to automatically resume when a query is submitted that requires the warehouse.
D: Increasing the maximum cluster count parameter for a multi-cluster virtual warehouse would potentially increase credit consumption by allowing more clusters to run simultaneously. It is used to scale up resources for performance, not to reduce costs.
Automating the operational times of virtual warehouses ensures that you only consume compute credits when the warehouse is actively being used for queries, thereby optimizing your Snowflake credit usage.
Reference: Snowflake Documentation on Managing Warehouse Credit Usage
(https://docs.snowflake.com/en/user-guide/warehouses-considerations.html#managing-warehouse-credit-usage)
NEW QUESTION # 75
While clustering a table, columns with which data types can be used as clustering keys? (Select TWO).
Answer: B,D
Explanation:
A clustering key can be defined when a table is created by appending a CLUSTER Where each clustering key consists of one or more table columns/expressions, which can be of any data type, except GEOGRAPHY, VARIANT, OBJECT, or ARRAY https://docs.snowflake.com/en/user-guide/tables-clustering-keys
NEW QUESTION # 76
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