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Snowflake ARA-C01 certification is an advanced-level certification exam that is designed to validate the skills and knowledge of the candidates in the field of Snowflake data warehousing. SnowPro Advanced Architect Certification certification exam is intended for experienced professionals who have a deep understanding of Snowflake architecture, its various components, and their respective functionalities. SnowPro Advanced Architect Certification certification exam is an ideal choice for architects, consultants, and data engineers who work with Snowflake on a daily basis.

Snowflake ARA-C01 exam covers a wide range of advanced topics related to Snowflake architecture, including data modeling, security, performance tuning, and data integration. Candidates are expected to have a deep understanding of Snowflake’s features and functionality, as well as the ability to apply this knowledge to real-world scenarios.

Snowflake ARA-C01 Exam is an advanced-level certification that is intended for individuals who have already achieved the SnowPro Core Certification. ARA-C01 exam is designed to evaluate the knowledge and expertise of professionals in areas such as data modeling, data integration, performance optimization, and security and governance.

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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q40-Q45):

NEW QUESTION # 40
What built-in Snowflake features make use of the change tracking metadata for a table? (Choose two.)

A. The CHANGE_DATA_CAPTURE command
B. The UPSERT command
C. The CHANGES clause
D. The MERGE command
E. A STREAM object

Answer: C,E

Explanation:
The built-in Snowflake features that make use of the change tracking metadata for a table are the CHANGES clause and a STREAM object. The CHANGES clause enables querying the change tracking metadata for a table or view within a specified interval of time without having to create a stream with an explicit transactional offset1. A STREAM object records data manipulation language (DML) changes made to tables, including inserts, updates, and deletes, as well as metadata about each change, so that actions can be taken using the changed data. This process is referred to as change data capture (CDC)2. The other options are incorrect because they do not make use of the change tracking metadata for a table. The MERGE command performs insert, update, or delete operations on a target table based on the results of a join with a source table3. The UPSERT command is not a valid Snowflake command. The CHANGE_DATA_CAPTURE command is not a valid Snowflake command. References: CHANGES | Snowflake Documentation, Change Tracking Using Table Streams | Snowflake Documentation, MERGE | Snowflake Documentation

NEW QUESTION # 41
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. Scaling up the virtual warehouses will support parallel processing of new source loads
C. Snowflake's ability to hash keys so that hash key joins can run faster than integer joins
D. Data needs to be pre-partitioned to obtain a superior data access performance

Answer: A

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 # 42
Who can provide permission to EXECUTE TASK?

A. THE TASK OWNER
B. ACCOUNTADMIN
C. SYSADMIN

Answer: B

NEW QUESTION # 43
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. Ask the partner to set up a Snowflake reader account and use that account to get the data for ingestion.
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. Keep the current structure but request that the partner stop changing files, instead only appending new files.

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 # 44
What actions are permitted when using the Snowflake SQL REST API? (Select TWO).

A. Submitting multiple SQL statements in a single call
B. The use of a PUT command
C. The use of a GET command
D. The use of a ROLLBACK command
E. The use of a CALL command to a stored procedure which returns a table

Answer: A,E

NEW QUESTION # 45
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