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Snowflake ARA-C01 Test Questions Answers & Exam Dumps ARA-C01 Free

You have to put in some extra effort, time, and investment and prepare well to pass this milestone. Do you have a plan to get success in the Snowflake ARA-C01 certification exam? Are you looking for the right study material that ensures your success in the DumpsQuestion new real Snowflake ARA-C01 Exam Questions on your first attempt? If your answer is yes then you just need to get help from DumpsQuestion practice exam questions.

Snowflake ARA-C01 certification is a valuable credential for architects and consultants who work with Snowflake. It demonstrates a deep understanding of Snowflake's architecture, data modeling, and performance tuning, which are essential skills for designing and implementing scalable data warehousing solutions. Employers also recognize the value of this certification, as it validates an individual's expertise and can help them stand out in a competitive job market.

Snowflake ARA-C01 exam is a rigorous assessment of an individual's technical abilities and understanding of Snowflake's architecture. ARA-C01 exam consists of multiple-choice questions and covers a broad range of topics, including data modeling, security, performance tuning, and integration with other systems. ARA-C01 Exam also tests an individual's ability to design and implement advanced Snowflake solutions that meet specific business requirements.

The SnowPro Advanced Architect Certification exam is designed to test the candidate's ability to design, implement, and manage secure, scalable, and reliable Snowflake solutions. ARA-C01 exam covers a broad range of topics, including Snowflake architecture, data modeling, security, performance optimization, scalability, and migration. Successful candidates will demonstrate their ability to design and implement Snowflake solutions that meet the complex data management needs of organizations.

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Exam Dumps ARA-C01 Free - ARA-C01 Actual Test Pdf

Passing the Snowflake ARA-C01 certification exam is necessary for professional development, and employing real ARA-C01 Exam Dumps can assist applicants in reaching their professional goals. These actual ARA-C01 questions assist students in discovering areas in which they need improvement, boost confidence, and lower anxiety. Candidates will breeze through SnowPro Advanced Architect Certification (ARA-C01) certification examination with flying colors and advance to the next level of their jobs if they prepare with updated ARA-C01 exam questions.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q98-Q103):

NEW QUESTION # 98
What is a key consideration when setting up search optimization service for a table?

A. Search optimization service works best with a column that has a minimum of 100 K distinct values.
B. Search optimization service can help to optimize storage usage by compressing the data into a GZIP format.
C. Search optimization service can significantly improve query performance on partitioned external tables.
D. The table must be clustered with a key having multiple columns for effective search optimization.

Answer: A

Explanation:
Explanation:

NEW QUESTION # 99
A global company needs to securely share its sales and Inventory data with a vendor using a Snowflake account.
The company has its Snowflake account In the AWS eu-west 2 Europe (London) region. The vendor's Snowflake account Is on the Azure platform in the West Europe region. How should the company's Architect configure the data share?

A. 1. Promote an existing database in the company's local account to primary.
2. Replicate the database to Snowflake on Azure in the West-Europe region.
3. Create a share and add objects to the share.
4. Add a consumer account to the share for the vendor to access.
B. 1. Create a share.
2. Add objects to the share.
3. Add a consumer account to the share for the vendor to access.
C. 1. Create a share.
2. Create a reader account for the vendor to use.
3. Add the reader account to the share.
D. 1. Create a new role called db_share.
2. Grant the db_share role privileges to read data from the company database and schema.
3. Create a user for the vendor.
4. Grant the ds_share role to the vendor's users.

Answer: B

Explanation:
The correct way to securely share data with a vendor using a Snowflake account on a different cloud platform and region is to create a share, add objects to the share, and add a consumer account to the share for the vendor to access. This way, the company can control what data is shared, who can access it, and how long the share is valid. The vendor can then query the shared data without copying or moving it to their own account. The other options are either incorrect or inefficient, as they involve creating unnecessary reader accounts, users, roles, or database replication.
https://learn.snowflake.com/en/certifications/snowpro-advanced-architect/

NEW QUESTION # 100
An Architect has a design where files arrive every 10 minutes and are loaded into a primary database table using Snowpipe. A secondary database is refreshed every hour with the latest data from the primary database.
Based on this scenario, what Time Travel query options are available on the secondary database?

A. Using Time Travel, secondary database users can query every iterative version within each hour (the individual Snowpipe loads) and outside the retention window.
B. Using Time Travel, secondary database users can query every iterative version within each hour (the individual Snowpipe loads) in the retention window.
C. A query using Time Travel in the secondary database is available for every hourly table version within and outside the retention window.
D. A query using Time Travel in the secondary database is available for every hourly table version within the retention window.

Answer: D

Explanation:
Snowflake's Time Travel feature allows users to query historical data within a defined retention period. In the given scenario, since the secondary database is refreshed every hour, Time Travel can be used to query each hourly version of the table as long as it falls within the retention window. This does not include individual Snowpipe loads within each hour unless they coincide with the hourly refresh.

NEW QUESTION # 101
When loading data into a table that captures the load time in a column with a default value of either CURRENT_TIME () or CURRENT_TIMESTAMP() what will occur?

A. All rows loaded using a specific COPY statement will have varying timestamps based on when the rows were inserted.
B. Any rows loaded using a specific COPY statement will have varying timestamps based on when the rows were read from the source.
C. All rows loaded using a specific COPY statement will have the same timestamp value.
D. Any rows loaded using a specific COPY statement will have varying timestamps based on when the rows were created in the source.

Answer: C

Explanation:
According to the Snowflake documentation, when loading data into a table that captures the load time in a column with a default value of either CURRENT_TIME () or CURRENT_TIMESTAMP(), the default value is evaluated once per COPY statement, not once per row. Therefore, all rows loaded using a specific COPY statement will have the same timestamp value. This behavior ensures that the timestamp value reflects the time when the data was loaded into the table, not when the data was read from the source or created in the source.
References:
* Snowflake Documentation: Loading Data into Tables with Default Values
* Snowflake Documentation: COPY INTO table

NEW QUESTION # 102
Which query will identify the specific days and virtual warehouses that would benefit from a multi-cluster warehouse to improve the performance of a particular workload?

Answer: C

Explanation:
The correct answer is option B. This query is designed to assess the need for a multi-cluster warehouse by examining the queuing time (AVG_QUEUED_LOAD) on different days and virtual warehouses. When the AVG_QUEUED_LOAD is greater than zero, it suggests that queries are waiting for resources, which can be an indicator that performance might be improved by using a multi-cluster warehouse to handle the workload more efficiently. By grouping by date and warehouse name and filtering on the sum of the average queued load being greater than zero, the query identifies specific days and warehouses where the workload exceeded the available compute resources. This information is valuable when considering scaling out warehouses to multi-cluster configurations for improved performance.

NEW QUESTION # 103
......

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