NEW ARA-C01 TEST CAMP | ARA-C01 VALID STUDY NOTES

New ARA-C01 Test Camp | ARA-C01 Valid Study Notes

New ARA-C01 Test Camp | ARA-C01 Valid Study Notes

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Snowflake ARA-C01 Certification Exam has a reputation for being one of the most challenging Snowflake certification exams. It requires a deep understanding of Snowflake architecture and features, as well as a strong ability to apply that knowledge to complex real-world scenarios. However, earning this certification can be a significant boost to a professional's career, as it demonstrates their expertise in architecting Snowflake solutions at an advanced level.

Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a highly reputable certification that is recognized globally by businesses and organizations that use Snowflake. SnowPro Advanced Architect Certification certification exam is designed to test the skills and knowledge of individuals who want to become advanced architects in data warehousing and data analytics. SnowPro Advanced Architect Certification certification is a valuable asset for individuals who want to advance their careers in these fields, and there are several resources available to help candidates prepare for the exam.

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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 SnowPro Advanced Architect Certification Sample Questions (Q141-Q146):

NEW QUESTION # 141
An Architect has chosen to separate their Snowflake Production and QA environments using two separate Snowflake accounts.
The QA account is intended to run and test changes on data and database objects before pushing those changes to the Production account. It is a requirement that all database objects and data in the QA account need to be an exact copy of the database objects, including privileges and data in the Production account on at least a nightly basis.
Which is the LEAST complex approach to use to populate the QA account with the Production account's data and database objects on a nightly basis?

  • A. 1) Create a share in the Production account for each database
    2) Share access to the QA account as a Consumer
    3) The QA account creates a database directly from each share
    4) Create clones of those databases on a nightly basis
    5) Run tests directly on those cloned databases
  • B. 1) Create a stage in the Production account
    2) Create a stage in the QA account that points to the same external object-storage location
    3) Create a task that runs nightly to unload each table in the Production account into the stage
    4) Use Snowpipe to populate the QA account
  • C. 1) In the Production account, create an external function that connects into the QA account and returns all the data for one specific table
    2) Run the external function as part of a stored procedure that loops through each table in the Production account and populates each table in the QA account
  • D. 1) Enable replication for each database in the Production account
    2) Create replica databases in the QA account
    3) Create clones of the replica databases on a nightly basis
    4) Run tests directly on those cloned databases

Answer: D

Explanation:
This approach is the least complex because it uses Snowflake's built-in replication feature to copy the data and database objects from the Production account to the QA account. Replication is a fast and efficient way to synchronize data across accounts, regions, and cloud platforms. It also preserves the privileges and metadata of the replicated objects. By creating clones of the replica databases, the QA account can run tests on the cloned data without affecting the original data. Clones are also zero-copy, meaning they do not consume any additional storage space unless the data is modified. This approach does not require any external stages, tasks, Snowpipe, or external functions, which can add complexity and overhead to the data transfer process.
Reference:
Introduction to Replication and Failover
Replicating Databases Across Multiple Accounts
Cloning Considerations


NEW QUESTION # 142
A DevOps team has a requirement for recovery of staging tables used in a complex set of data pipelines. The staging tables are all located in the same staging schem a. One of the requirements is to have online recovery of data on a rolling 7-day basis.
After setting up the DATA_RETENTION_TIME_IN_DAYS at the database level, certain tables remain unrecoverable past 1 day.
What would cause this to occur? (Choose two.)

  • A. The staging tables are of the TRANSIENT type.
  • B. The DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day.
  • C. The staging schema has not been setup for MANAGED ACCESS.
  • D. The tables exceed the 1 TB limit for data recovery.
  • E. The DevOps role should be granted ALLOW_RECOVERY privilege on the staging schema.

Answer: A,B

Explanation:
The DATA_RETENTION_TIME_IN_DAYS parameter controls the Time Travel retention period for an object (database, schema, or table) in Snowflake. This parameter specifies the number of days for which historical data is preserved and can be accessed using Time Travel operations (SELECT, CREATE ... CLONE, UNDROP)1.
The requirement for recovery of staging tables on a rolling 7-day basis means that the DATA_RETENTION_TIME_IN_DAYS parameter should be set to 7 at the database level. However, this parameter can be overridden at the lower levels (schema or table) if they have a different value1.
Therefore, one possible cause for certain tables to remain unrecoverable past 1 day is that the DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day. This would override the database level setting and limit the Time Travel retention period for all the tables in the schema to 1 day. To fix this, the parameter should be unset or set to 7 at the schema level1. Therefore, option B is correct.
Another possible cause for certain tables to remain unrecoverable past 1 day is that the staging tables are of the TRANSIENT type. Transient tables are tables that do not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day. Transient tables are suitable for temporary or intermediate data that can be easily reproduced or replicated2. To fix this, the tables should be created as permanent tables, which can have a Time Travel retention period of up to 90 days1. Therefore, option D is correct.
Option A is incorrect because the MANAGED ACCESS feature is not related to the data recovery requirement. MANAGED ACCESS is a feature that allows granting access privileges to objects without explicitly granting the privileges to roles. It does not affect the Time Travel retention period or the data availability3.
Option C is incorrect because there is no 1 TB limit for data recovery in Snowflake. The data storage size does not affect the Time Travel retention period or the data availability4.
Option E is incorrect because there is no ALLOW_RECOVERY privilege in Snowflake. The privilege required to perform Time Travel operations is SELECT, which allows querying historical data in tables5.


NEW QUESTION # 143
Which Snowflake data modeling approach is designed for BI queries?

  • A. Snowflake schema
  • B. Star schema
  • C. 3 NF
  • D. Data Vault

Answer: A


NEW QUESTION # 144
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. Data needs to be pre-partitioned to obtain a superior data access performance
  • C. Snowflake's ability to hash keys so that hash key joins can run faster than integer joins
  • D. Scaling up the virtual warehouses will support parallel processing of new source loads

Answer: A,D

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 # 145
What does Percentage scanned from cache in the query profile signify?

  • A. The percentage of data scanned from the local disk cache
  • B. The percentage of data scanned from the QUERY cache
  • C. The percentage of data scanned from the METADATA cache

Answer: A


NEW QUESTION # 146
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