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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Exam is a professional certification exam offered by Snowflake for individuals who wish to demonstrate advanced knowledge and skills in designing and implementing Snowflake solutions. ARA-C01 Exam is the highest-level certification offered by Snowflake and is designed for architects, engineers, and consultants who work with Snowflake on a daily basis.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q125-Q130):

NEW QUESTION # 125
A retail company has 2000+ stores spread across the country. Store Managers report that they are having trouble running key reports related to inventory management, sales targets, payroll, and staffing during business hours. The Managers report that performance is poor and time-outs occur frequently.
Currently all reports share the same Snowflake virtual warehouse.
How should this situation be addressed? (Select TWO).

A. Configure a dedicated virtual warehouse for the Store Manager team.
B. Use a Business Intelligence tool for in-memory computation to improve performance.
C. Advise the Store Manager team to defer report execution to off-business hours.
D. Configure the virtual warehouse to size 4-XL
E. Configure the virtual warehouse to be multi-clustered.

Answer: A,E

Explanation:
The best way to address the performance issues and time-outs faced by the Store Manager team is to configure a dedicated virtual warehouse for them and make it multi-clustered. This will allow them to run their reports independently from other workloads and scale up or down the compute resources as needed. A dedicated virtual warehouse will also enable them to apply specific security and access policies for their data. A multi-clustered virtual warehouse will provide high availability and concurrency for their queries and avoid queuing or throttling.
Using a Business Intelligence tool for in-memory computation may improve performance, but it will not solve the underlying issue of insufficient compute resources in the shared virtual warehouse. It will also introduce additional costs and complexity for the data architecture.
Configuring the virtual warehouse to size 4-XL may increase the performance, but it will also increase the cost and may not be optimal for the workload. It will also not address the concurrency and availability issues that may arise from sharing the virtual warehouse with other workloads.
Advising the Store Manager team to defer report execution to off-business hours may reduce the load on the shared virtual warehouse, but it will also reduce the timeliness and usefulness of the reports for the business. It will also not guarantee that the performance issues and time-outs will not occur at other times.
References:
* Snowflake Architect Training
* Snowflake SnowPro Advanced Architect Certification - Preparation Guide
* SnowPro Advanced: Architect Exam Study Guide

NEW QUESTION # 126
What Snowflake features should be leveraged when modeling using Data Vault?

A. Snowflake's support of multi-table inserts into the data model's Data Vault tables
B. Snowflake's ability to hash keys so that hash key joins can run faster than integer joins
C. Scaling up the virtual warehouses will support parallel processing of new source loads
D. Data needs to be pre-partitioned to obtain a superior data access performance

Answer: A,C

Explanation:
These two features are relevant for modeling using Data Vault on Snowflake. Data Vault is a data modeling approach that organizes data into hubs, links, and satellites. Data Vault is designed to enable high scalability, flexibility, and performance for data integration and analytics. Snowflake is a cloud data platform that supports various data modeling techniques, including Data Vault. Snowflake provides some features that can enhance the Data Vault modeling, such as:
* Snowflake's support of multi-table inserts into the data model's Data Vault tables. Multi-table inserts (MTI) are a feature that allows inserting data from a single query into multiple tables in a single DML statement. MTI can improve the performance and efficiency of loading data into Data Vault tables, especially for real-time or near-real-time data integration. MTI can also reduce the complexity and maintenance of the loading code, as well as the data duplication and latency12.
* Scaling up the virtual warehouses will support parallel processing of new source loads. Virtual
* warehouses are a feature that allows provisioning compute resources on demand for data processing.
Virtual warehouses can be scaled up or down by changing the size of the warehouse, which determines the number of servers in the warehouse. Scaling up the virtual warehouses can improve the performance and concurrency of processing new source loads into Data Vault tables, especially for large or complex data sets. Scaling up the virtual warehouses can also leverage the parallelism and distribution of Snowflake's architecture, which can optimize the data loading and querying34.
References:
* Snowflake Documentation: Multi-table Inserts
* Snowflake Blog: Tips for Optimizing the Data Vault Architecture on Snowflake
* Snowflake Documentation: Virtual Warehouses
* Snowflake Blog: Building a Real-Time Data Vault in Snowflake

NEW QUESTION # 127
What are purposes for creating a storage integration? (Choose three.)

A. Manage credentials from multiple cloud providers in one single Snowflake object.
B. Create private VPC endpoints that allow direct, secure connectivity between VPCs without traversing the public internet.
C. Support multiple external stages using one single Snowflake object.
D. Avoid supplying credentials when creating a stage or when loading or unloading data.
E. Control access to Snowflake data using a master encryption key that is maintained in the cloud provider's key management service.
F. Store a generated identity and access management (IAM) entity for an external cloud provider regardless of the cloud provider that hosts the Snowflake account.

Answer: C,D,F

Explanation:
The purpose of creating a storage integration in Snowflake includes:B. Store a generated identity and access management (IAM) entity for an external cloud provider - This helps in managing authentication and authorization with external cloud storage without embedding credentials in Snowflake. It supports various cloud providers like AWS, Azure, or GCP, ensuring that the identity management is streamlined across platforms.C. Support multiple external stages using one single Snowflake object - Storage integrations allow you to set up access configurations that can be reused across multiple external stages, simplifying the management of external data integrations.D. Avoid supplying credentials when creating a stage or when loading or unloading data - By using a storage integration, Snowflake can interact with external storage without the need to continuously manage or expose sensitive credentials, enhancing security and ease of operations.References: Snowflake documentation on storage integrations, found within the SnowPro Advanced: Architect course materials.

NEW QUESTION # 128
How can the Snowpipe REST API be used to keep a log of data load history?

A. Call insertReport every 20 minutes, fetching the last 10,000 entries.
B. Call insertReport every 8 minutes for a 10-minute time range.
C. Call loadHistoryScan every 10 minutes for a 15-minute time range.
D. Call loadHistoryScan every minute for the maximum time range.

Answer: C

Explanation:
Snowpipe is a service that automates and optimizes the loading of data from external stages into Snowflake tables. Snowpipe uses a queue to ingest files as they become available in the stage. Snowpipe also provides REST endpoints to load data and retrieve load history reports1.
The loadHistoryScan endpoint returns the history of files that have been ingested by Snowpipe within a specified time range. The endpoint accepts the following parameters2:
pipe: The fully-qualified name of the pipe to query.
startTimeInclusive: The start of the time range to query, in ISO 8601 format. The value must be within the past 14 days.
endTimeExclusive: The end of the time range to query, in ISO 8601 format. The value must be later than the start time and within the past 14 days.
recentFirst: A boolean flag that indicates whether to return the most recent files first or last. The default value is false, which means the oldest files are returned first.
showSkippedFiles: A boolean flag that indicates whether to include files that were skipped by Snowpipe in the response. The default value is false, which means only files that were loaded are returned.
The loadHistoryScan endpoint can be used to keep a log of data load history by calling it periodically with a suitable time range. The best option among the choices is D, which is to call loadHistoryScan every 10 minutes for a 15-minute time range. This option ensures that the endpoint is called frequently enough to capture the latest files that have been ingested, and that the time range is wide enough to avoid missing any files that may have been delayed or retried by Snowpipe. The other options are either too infrequent, too narrow, or use the wrong endpoint3.
Reference:
1: Introduction to Snowpipe | Snowflake Documentation
2: loadHistoryScan | Snowflake Documentation
3: Monitoring Snowpipe Load History | Snowflake Documentation

NEW QUESTION # 129
External functions must be scalar functions

A. FALSE
B. TRUE

Answer: B

NEW QUESTION # 130
......

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