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ARA-C01 Learning Materials: SnowPro Advanced Architect Certification& ARA-C01 Exam braindumps

To attempt the Snowflake ARA-C01 exam optimally and ace it on the first attempt, proper exam planning is crucial. Since the SnowPro Advanced Architect Certification (ARA-C01) exam demands a lot of time and effort, we designed the SnowPro Advanced Architect Certification (ARA-C01) exam dumps in such a way that you won't have to go through sleepless study nights or disturb your schedule. Before starting the SnowPro Advanced Architect Certification (ARA-C01) preparation, plan the amount of time you will allot to each topic, determine the topics that demand more effort and prioritize the components that possess more weightage in the SnowPro Advanced Architect Certification (ARA-C01) exam.

Snowflake ARA-C01 (SnowPro Advanced Architect Certification) is a professional certification exam designed to validate the skills and knowledge of data architects and engineers who specialize in designing and implementing complex Snowflake cloud data platforms. SnowPro Advanced Architect Certification certification exam is recognized globally by companies that use Snowflake for their data management, warehousing, and analytics needs.

To qualify for the Snowflake ARA-C01 exam, candidates must first obtain the SnowPro Advanced Architect certification. SnowPro Advanced Architect Certification certification requires passing the SnowPro Core exam, as well as demonstrating proficiency in data modeling and performance tuning. Once candidates have achieved this certification, they can then take the ARA-C01 Exam to further validate their expertise.

Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a rigorous certification exam that is designed to test an individual's proficiency in the technical aspects of the Snowflake platform. SnowPro Advanced Architect Certification certification exam is targeted at experienced architects and engineers who are looking to validate their knowledge and skills in designing and implementing advanced Snowflake solutions. The Snowflake ARA-C01 exam covers a wide range of topics, including Snowflake architecture, security, performance tuning, data warehousing, and data integration.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q112-Q117):

NEW QUESTION # 112
An Architect needs to allow a user to create a database from an inbound share.
To meet this requirement, the user's role must have which privileges? (Choose two.)

A. IMPORT DATABASE;
B. IMPORT PRIVILEGES;
C. CREATE DATABASE;
D. CREATE SHARE;
E. IMPORT SHARE;

Answer: B,C

NEW QUESTION # 113
Files arrive in an external stage every 10 seconds from a proprietary system. The files range in size from 500 K to 3 MB. The data must be accessible by dashboards as soon as it arrives.
How can a Snowflake Architect meet this requirement with the LEAST amount of coding? (Choose two.)

A. Use a COPY command with a task.
B. Use a materialized view on an external table.
C. Use the COPY INTO command.
D. Use Snowpipe with auto-ingest.
E. Use a combination of a task and a stream.

Answer: B,D

Explanation:
These two options are the best ways to meet the requirement of loading data from an external stage and making it accessible by dashboards with the least amount of coding.
Snowpipe with auto-ingest is a feature that enables continuous and automated data loading from an external stage into a Snowflake table. Snowpipe uses event notifications from the cloud storage service to detect new or modified files in the stage and triggers a COPY INTO command to load the data into the table. Snowpipe is efficient, scalable, and serverless, meaning it does not require any infrastructure or maintenance from the user. Snowpipe also supports loading data from files of any size, as long as they are in a supported format1.
A materialized view on an external table is a feature that enables creating a pre-computed result set from an external table and storing it in Snowflake. A materialized view can improve the performance and efficiency of querying data from an external table, especially for complex queries or dashboards. A materialized view can also support aggregations, joins, and filters on the external table data. A materialized view on an external table is automatically refreshed when the underlying data in the external stage changes, as long as the AUTO_REFRESH parameter is set to true2.
Reference:
Snowpipe Overview | Snowflake Documentation
Materialized Views on External Tables | Snowflake Documentation

NEW QUESTION # 114
Data replication in snowflake helps in

A. Data sharing
B. Disaster recovery
C. Fail safe
D. Account Migration

Answer: A,B,D

NEW QUESTION # 115
A Snowflake Architect is designing an application and tenancy strategy for an organization where strong legal isolation rules as well as multi-tenancy are requirements.
Which approach will meet these requirements if Role-Based Access Policies (RBAC) is a viable option for isolating tenants?

A. Create accounts for each tenant in the Snowflake organization.
B. Create a multi-tenant table strategy if row level security is not viable for isolating tenants.
C. Create an object for each tenant strategy if row level security is not viable for isolating tenants.
D. Create an object for each tenant strategy if row level security is viable for isolating tenants.

Answer: A

Explanation:
This approach meets the requirements of strong legal isolation and multi-tenancy. By creating separate accounts for each tenant, the application can ensure that each tenant has its own dedicated storage, compute, and metadata resources, as well as its own encryption keys and security policies. This provides the highest level of isolation and data protection among the tenancy models. Furthermore, by creating the accounts within the same Snowflake organization, the application can leverage the features of Snowflake Organizations, such as centralized billing, account management, and cross-account data sharing.
References:
* Snowflake Organizations Overview | Snowflake Documentation
* Design Patterns for Building Multi-Tenant Applications on Snowflake

NEW QUESTION # 116
A Developer is having a performance issue with a Snowflake query. The query receives up to 10 different values for one parameter and then performs an aggregation over the majority of a fact table. It then joins against a smaller dimension table. This parameter value is selected by the different query users when they execute it during business hours. Both the fact and dimension tables are loaded with new data in an overnight import process.
On a Small or Medium-sized virtual warehouse, the query performs slowly. Performance is acceptable on a size Large or bigger warehouse. However, there is no budget to increase costs. The Developer needs a recommendation that does not increase compute costs to run this query.
What should the Architect recommend?

A. Create a task that will run the 10 different variations of the query corresponding to the 10 different parameters before the users come in to work. The task will be scheduled to align with the users' working hours in order to allow the warehouse cache to be used.
B. Enable the search optimization service on the table. When the users execute the query, the search optimization service will automatically adjust the query execution plan based on the frequently-used parameters.
C. Create a dedicated size Large warehouse for this particular set of queries. Create a new role that has USAGE permission on this warehouse and has the appropriate read permissions over the fact and dimension tables. Have users switch to this role and use this warehouse when they want to access this data.
D. Create a task that will run the 10 different variations of the query corresponding to the 10 different parameters before the users come in to work. The query results will then be cached and ready to respond quickly when the users re-issue the query.

Answer: B

Explanation:
Enabling the search optimization service on the table can improve the performance of queries that have selective filtering criteria, which seems to be the case here. This service optimizes the execution of queries by creating a persistent data structure called a search access path, which allows some micro-partitions to be skipped during the scanning process. This can significantly speed up query performance without increasing compute costs1.
Reference
* Snowflake Documentation on Search Optimization Service1.

NEW QUESTION # 117
......

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