SNOWFLAKE ARA-C01 EXAM QUESTIONS - 1 YEAR OF FREE UPDATES

Snowflake ARA-C01 Exam Questions - 1 year of Free Updates

Snowflake ARA-C01 Exam Questions - 1 year of Free Updates

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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a globally recognized certification program that validates an individual's expertise in designing and implementing Snowflake solutions. SnowPro Advanced Architect Certification certification is designed for experienced architects who have already achieved the SnowPro Core Certification and have a deep understanding of Snowflake's data warehousing platform. The Snowflake ARA-C01 Exam covers a wide range of advanced topics, including Snowflake architecture, query optimization, data modeling, security, and performance tuning.

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A good ARA-C01 certification must be supported by a good ARA-C01 exam practice, which will greatly improve your learning ability and effectiveness. Our study materials have the advantage of short time, high speed and high pass rate. You only take 20 to 30 hours to practice our ARA-C01 Guide materials and then you can take the exam. If you use our study materials, you can get the ARA-C01 certification by spending very little time and energy reviewing and preparing.

Snowflake ARA-C01 Certification Exam is an advanced-level exam that requires a deep understanding of Snowflake's architecture and best practices. ARA-C01 exam is designed to test an individual's ability to design and build scalable, secure, and high-performing data solutions on the Snowflake platform. ARA-C01 exam is intended for professionals who have several years of experience in data architecture and engineering and are seeking to validate their skills and demonstrate their expertise in the Snowflake ecosystem. Passing the SnowPro Advanced Architect Certification Exam can help professionals gain recognition in the industry and demonstrate their competence to potential employers.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q22-Q27):

NEW QUESTION # 22
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 an object for each tenant strategy if row level security is viable for isolating tenants.
  • B. Create an object for each tenant strategy if row level security is not viable for isolating tenants.
  • C. Create a multi-tenant table strategy if row level security is not viable for isolating tenants.
  • D. Create accounts for each tenant in the Snowflake organization.

Answer: D


NEW QUESTION # 23
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 schema has not been setup for MANAGED ACCESS.
  • B. The tables exceed the 1 TB limit for data recovery.
  • C. The DATA_RETENTION_TIME_IN_DAYS for the staging schema has been set to 1 day.
  • D. The staging tables are of the TRANSIENT type.
  • E. The DevOps role should be granted ALLOW_RECOVERY privilege on the staging schema.

Answer: C,D

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 # 24
The following DDL command was used to create a task based on a stream:

Assuming MY_WH is set to auto_suspend - 60 and used exclusively for this task, which statement is true?

  • A. The warehouse MY_WH will be made active every five minutes to check the stream.
  • B. The warehouse MY_WH will only be active when there are results in the stream.
  • C. The warehouse MY_WH will automatically resize to accommodate the size of the stream.
  • D. The warehouse MY_WH will never suspend.

Answer: A


NEW QUESTION # 25
A media company needs a data pipeline that will ingest customer review data into a Snowflake table, and apply some transformations. The company also needs to use Amazon Comprehend to do sentiment analysis and make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions.
The data pipeline needs to run continuously and efficiently as new records arrive in the object storage leveraging event notifications. Also, the operational complexity, maintenance of the infrastructure, including platform upgrades and security, and the development effort should be minimal.
Which design will meet these requirements?

  • A. Ingest the data into Snowflake using Amazon EMR and PySpark using the Snowflake Spark connector. Apply transformations using another Spark job. Develop a python program to do model inference by leveraging the Amazon Comprehend text analysis API. Then write the results to a Snowflake table and create a listing in the Snowflake Marketplace to make the data available to other companies.
  • B. Ingest the data using copy into and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.
  • C. Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.
  • D. Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Create an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.

Answer: D

Explanation:
Option B is the best design to meet the requirements because it uses Snowpipe to ingest the data continuously and efficiently as new records arrive in the object storage, leveraging event notifications. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables1. It also uses streams and tasks to orchestrate transformations on the ingested data. Streams are objects that store the change history of a table, and tasks are objects that execute SQL statements on a schedule or when triggered by another task2. Option B also uses an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. An external function is a user-defined function that calls an external API, such as Amazon Comprehend, to perform computations that are not natively supported by Snowflake3. Finally, option B uses the Snowflake Marketplace to make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions. The Snowflake Marketplace is a platform that enables data providers to list and share their data sets with data consumers, regardless of the cloud platform or region they use4.
Option A is not the best design because it uses copy into to ingest the data, which is not as efficient and continuous as Snowpipe. Copy into is a SQL command that loads data from files into a table in a single transaction. It also exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
Option C is not the best design because it uses Amazon EMR and PySpark to ingest and transform the data, which also increases the operational complexity and maintenance of the infrastructure. Amazon EMR is a cloud service that provides a managed Hadoop framework to process and analyze large-scale data sets. PySpark is a Python API for Spark, a distributed computing framework that can run on Hadoop. Option C also develops a python program to do model inference by leveraging the Amazon Comprehend text analysis API, which increases the development effort.
Option D is not the best design because it is identical to option A, except for the ingestion method. It still exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.


NEW QUESTION # 26
A table for IOT devices that measures water usage is created. The table quickly becomes large and contains more than 2 billion rows.

The general query patterns for the table are:
1. DeviceId, lOT_timestamp and Customerld are frequently used in the filter predicate for the select statement
2. The columns City and DeviceManuf acturer are often retrieved
3. There is often a count on Uniqueld
Which field(s) should be used for the clustering key?

  • A. lOT_timestamp
  • B. City and DeviceManuf acturer
  • C. Deviceld and Customerld
  • D. Uniqueld

Answer: C


NEW QUESTION # 27
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