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| Section | Objectives |
|---|---|
| Topic 1: Data Loading and Unloading | - Data unloading and external stages - Bulk loading using stages and COPY INTO |
| Topic 2: Snowflake Architecture and Overview | - Snowflake architecture concepts (cloud data platform, separation of storage and compute) - Virtual warehouses and resource scaling concepts |
| Topic 3: Data Transformations and Querying | - SQL querying in Snowflake - Semi-structured data handling (VARIANT, JSON, Parquet) |
| Topic 4: Data Protection, Security, and Performance | - Query performance optimization basics - Role-based access control (RBAC) - Time Travel and Fail-safe concepts |
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NEW QUESTION # 132
Which Snowflake data types can be used to build nested hierarchical data? (Select TWO)
Answer: C,E
Explanation:
The Snowflake data types that can be used to build nested hierarchical data are OBJECT and VARIANT. These data types support the storage and querying of semi-structured data, allowing for the creation of complex, nested data structures
NEW QUESTION # 133
What criteria does Snowflake use to determine the current role when initiating a session? (Select TWO).
Answer: A,D
NEW QUESTION # 134
A Snowflake user needs to share a data set and wants it to be available to any Snowflake account that resides in the same cloud region. What is the recommended way to do this?
Answer: D
Explanation:
The correct answer is A. Use a public listing .
A public listing on Snowflake Marketplace allows a provider to make a data product discoverable and available to Snowflake consumers. If the goal is to make a dataset available broadly to Snowflake accounts in the same supported region, a public listing is the recommended sharing method.
Why A is correct:
Public listings are designed for broad distribution of datasets, applications, or services to Snowflake consumers. They are more scalable and discoverable than direct shares when the target audience is many or any Snowflake accounts.
Why the other options are incorrect:
B). A direct share is used to share data with specific known consumer accounts, not broadly with any account in a region.
C). A Data Exchange is a private data hub for a controlled group of members, not the best method for making data broadly available to any Snowflake account.
D). An external stage is used to reference files in cloud storage. It is not Snowflake's recommended secure sharing mechanism for making datasets available to Snowflake accounts.
Official Snowflake documentation reference:
Snowflake documentation describes listings as the mechanism for offering data products to consumers through Snowflake Marketplace, while direct shares are for sharing with specific accounts.
Reference: Snowflake Documentation - Listings; Snowflake Marketplace; Secure Data Sharing; SnowPro Core Study Guide - Data Protection and Governance.
NEW QUESTION # 135
Which of the following describes a Snowflake stored procedure?
Answer: D
Explanation:
Reference: https://docs.snowflake.com/en/sql-reference/stored-procedures-overview.html
NEW QUESTION # 136
What action should be taken if a large number of concurrent queries are queued in a virtual warehouse?
Answer: C
Explanation:
When a large number of concurrent queries are queued in a virtual warehouse in Snowflake, the most effective action is to scale out using a multi-cluster warehouse. Here's why:
* Understanding the Problem:
* When many queries are queued, it indicates that the computational resources of the warehouse are insufficient to handle the demand.
* Each virtual warehouse in Snowflake provides a fixed amount of compute resources. A single cluster in the warehouse can only process a finite number of concurrent queries efficiently.
* Scale-Out with Multi-Cluster Warehouse:
* Snowflake's multi-cluster warehouse feature allows a warehouse to dynamically add or remove clusters based on the current workload.
* With scale-out, additional clusters are automatically spun up when the query load increases. This helps distribute the workload and reduces queuing without impacting ongoing query performance.
* Once the load decreases, the additional clusters are automatically suspended, optimizing costs.
* Why Not Other Options?:
* A. Scale-up by resizing the warehouse: While scaling up (choosing a larger size, e.g., from X- Small to Medium) increases compute power, it may not efficiently resolve queuing when many concurrent queries need to be processed. It's better suited for improving the performance of individual queries rather than handling more queries simultaneously.
* C. Disable auto-suspend on the warehouse: This ensures the warehouse remains active but does not address the queuing issue. Keeping a warehouse perpetually active can lead to unnecessary costs.
* D. Enable auto-resume on the warehouse: This ensures the warehouse activates when needed but does not directly impact query concurrency.
* Best Practices for Scaling with Multi-Cluster Warehouses:
* Configure the minimum and maximum cluster settings to allow Snowflake to handle varying workloads efficiently.
* Use auto-scaling policies like "economy" or "standard" to control cluster behavior based on cost or performance requirements.
* Steps to Implement Multi-Cluster Warehouse:
* Navigate to the Snowflake console.
* Go to the "Warehouses" tab and select the target warehouse.
* Edit the warehouse settings to set:
* Minimum Clusters: The minimum number of active clusters.
* Maximum Clusters: The upper limit of clusters that Snowflake can scale out to.
* Auto-Resume and Auto-Suspend: Ensure these are enabled for cost efficiency.
References:
* Snowflake Documentation: Scaling a Multi-Cluster Warehouse
* Snowflake Documentation: Query Processing and Queuing
* Snowflake Best Practices for Warehouses
NEW QUESTION # 137
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