ARA-C01 RELIABLE TEST BRAINDUMPS, ARA-C01 PDF DUMPS FILES

ARA-C01 Reliable Test Braindumps, ARA-C01 PDF Dumps Files

ARA-C01 Reliable Test Braindumps, ARA-C01 PDF Dumps Files

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To prepare for the Snowflake ARA-C01 Certification Exam, candidates must have a deep understanding of Snowflake architecture and data warehousing principles. They should also have experience with Snowflake administration, performance optimization, and security. There are several resources available to help candidates prepare for the exam, including Snowflake documentation, training courses, and practice exams.

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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q53-Q58):

NEW QUESTION # 53
Which steps are recommended best practices for prioritizing cluster keys in Snowflake? (Choose two.)

  • A. Choose cluster columns that are most actively used in selective filters.
  • B. Choose columns that are frequently used in join predicates.
  • C. Choose TIMESTAMP columns with nanoseconds for the highest number of unique rows.
  • D. Choose cluster columns that are actively used in the GROUP BY clauses.
  • E. Choose lower cardinality columns to support clustering keys and cost effectiveness.

Answer: A,B

Explanation:
According to the Snowflake documentation, the best practices for choosing clustering keys are:
Choose columns that are frequently used in join predicates. This can improve the join performance by reducing the number of micro-partitions that need to be scanned and joined.
Choose columns that are most actively used in selective filters. This can improve the scan efficiency by skipping micro-partitions that do not match the filter predicates.
Avoid using low cardinality columns, such as gender or country, as clustering keys. This can result in poor clustering and high maintenance costs.
Avoid using TIMESTAMP columns with nanoseconds, as they tend to have very high cardinality and low correlation with other columns. This can also result in poor clustering and high maintenance costs.
Avoid using columns with duplicate values or NULLs, as they can cause skew in the clustering and reduce the benefits of pruning.
Cluster on multiple columns if the queries use multiple filters or join predicates. This can increase the chances of pruning more micro-partitions and improve the compression ratio.
Clustering is not always useful, especially for small or medium-sized tables, or tables that are not frequently queried or updated. Clustering can incur additional costs for initially clustering the data and maintaining the clustering over time.
Reference:
Clustering Keys & Clustered Tables | Snowflake Documentation
[Considerations for Choosing Clustering for a Table | Snowflake Documentation]


NEW QUESTION # 54
You ran the below query and it took a long time to run
select itembarcode from checkouts where BIBNUMBER = '2213435';
The clustering information shows the below result.
What can you derive from this information
select system$clustering_information('checkouts','(BIBNUMBER)');

  • A. The data is not clustered well by BIBNUMBER and it is spread across all the micro-partitions, hence to retrieve a small number of micro-partitions, the query has to scan all the partitions in the table
  • B. The query is running slow because BIBNUMBER does not have an index created on it.
  • C. The query is running slow because the warehouse does not have enough memory

Answer: A


NEW QUESTION # 55
A Snowflake Architect is designing a multi-tenant application strategy for an organization in the Snowflake Data Cloud and is considering using an Account Per Tenant strategy.
Which requirements will be addressed with this approach? (Choose two.)

  • A. Compute costs must be optimized.
  • B. Storage costs must be optimized.
  • C. There needs to be fewer objects per tenant.
  • D. Tenant data shape may be unique per tenant.
  • E. Security and Role-Based Access Control (RBAC) policies must be simple to configure.

Answer: B,D

Explanation:
Explanation
* An Account Per Tenant strategy means creating a separate Snowflake account for each tenant (customer or business unit) of the multi-tenant application.
* This approach has some advantages and disadvantages compared to other strategies, such as Database Per Tenant or Schema Per Tenant.
* One advantage is that each tenant can have a unique data shape, meaning they can define their own tables, views, and other objects without affecting other tenants. This allows for more flexibility and customization for each tenant. Therefore, option D is correct.
* Another advantage is that storage costs can be optimized, because each tenant can use their own storage credits and manage their own data retention policies. This also reduces the risk of data spillover or cross-tenant access. Therefore, option E is correct.
* However, this approach also has some drawbacks, such as:
* It requires more administrative overhead and complexity to manage multiple accounts and their resources.
* It may not optimize compute costs, because each tenant has to provision their own warehouses and pay for their own compute credits. This may result in underutilization or overprovisioning of compute resources. Therefore, option C is incorrect.
* It may not simplify security and RBAC policies, because each account has to define its own roles, users, and privileges. This may increase the risk of human errors or inconsistencies in security configurations. Therefore, option B is incorrect.
* It may not reduce the number of objects per tenant, because each tenant still has to create their own databases, schemas, and other objects within their account. This mayaffect the performance and scalability of the application. Therefore, option A is incorrect.
References: : Multi-Tenant Application Strategies


NEW QUESTION # 56
Company A has recently acquired company B. The Snowflake deployment for company B is located in the Azure West Europe region.
As part of the integration process, an Architect has been asked to consolidate company B's sales data into company A's Snowflake account which is located in the AWS us-east-1 region.
How can this requirement be met?

  • A. Export the sales data from company B's Snowflake account as CSV files, and transfer the files to company A's Snowflake account. Import the data using Snowflake's data loading capabilities.
  • B. Migrate company B's Snowflake deployment to the same region as company A's Snowflake deployment, ensuring data locality. Then perform a direct database-to-database merge of the sales data.
  • C. Replicate the sales data from company B's Snowflake account into company A's Snowflake account using cross-region data replication within Snowflake. Configure a direct share from company B's account to company A's account.
  • D. Build a custom data pipeline using Azure Data Factory or a similar tool to extract the sales data from company B's Snowflake account. Transform the data, then load it into company A's Snowflake account.

Answer: B


NEW QUESTION # 57
A company's Architect needs to find an efficient way to get data from an external partner, who is also a Snowflake user. The current solution is based on daily JSON extracts that are placed on an FTP server and uploaded to Snowflake manually. The files are changed several times each month, and the ingestion process needs to be adapted to accommodate these changes.
What would be the MOST efficient solution?

  • A. Keep the current structure but request that the partner stop changing files, instead only appending new files.
  • B. Ask the partner to use the data lake export feature and place the data into cloud storage where Snowflake can natively ingest it (schema-on-read).
  • C. Ask the partner to create a share and add the company's account.
  • D. Ask the partner to set up a Snowflake reader account and use that account to get the data for ingestion.

Answer: C

Explanation:
The most efficient solution is to ask the partner to create a share and add the company's account (Option A).
This way, the company can access the live data from the partner without any data movement or manual intervention. Snowflake's secure data sharing feature allows data providers to share selected objects in a database with other Snowflake accounts. The shared data is read-only and does not incur any storage or compute costs for the data consumers. The data consumers can query the shared data directly or create local copies of the shared objects in their own databases. Option B is not efficient because it involves using the data lake export feature, which is intended for exporting data from Snowflake to an external data lake, not for importing data from another Snowflake account. The data lake export feature also requires the data provider to create an external stage on cloud storage and use the COPY INTO <location> command to export the data into parquet files. The data consumer then needs to create an external table or a file format to load the data from the cloud storage into Snowflake. This process can be complex and costly, especially if the data changes frequently. Option C is not efficient because it does not solve the problem of manual data ingestion and adaptation. Keeping the current structure of daily JSON extracts on an FTP server and requesting the partner to stop changing files, instead only appending new files, does not improve the efficiency or reliability of the data ingestion process. The company still needs to upload the data to Snowflake manually and deal with any schema changes or data quality issues. Option D is not efficient because it requires the partner to set up a Snowflake reader account and use that account to get the data for ingestion. A reader account is a special type of account that can only consume data from the provider account that created it. It is intended for data consumers who are not Snowflake customers and do not have a licensing agreement with Snowflake. A reader account is not suitable for data ingestion from another Snowflake account, as it does not allow uploading, modifying, or unloading data. The company would need to use external tools or interfaces to access the data from the reader account and load it into their own account, which can be slow and expensive. References: The answer can be verified from Snowflake's official documentation on secure data sharing, data lake export, and reader accounts available on their website. Here are some relevant links:
* Introduction to Secure Data Sharing | Snowflake Documentation
* Data Lake Export Public Preview Is Now Available on Snowflake | Snowflake Blog
* Managing Reader Accounts | Snowflake Documentation


NEW QUESTION # 58
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