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Snowflake ARA-C01 Certification Exam consists of 100 multiple-choice questions that are designed to test the knowledge and skills of the candidates across various domains such as data modeling, data loading, performance tuning, security and access control, and data processing. ARA-C01 exam is conducted in a timed format and candidates are expected to complete it within two hours. SnowPro Advanced Architect Certification certification exam is proctored and can be taken online from anywhere in the world.

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.

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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is an industry-recognized certification that validates the advanced skills and knowledge of professionals in designing and implementing Snowflake solutions. It is a comprehensive exam that covers a wide range of topics, including Snowflake architecture, security, performance optimization, data modeling, and integration with other technologies. SnowPro Advanced Architect Certification certification is ideal for professionals who are responsible for managing Snowflake solutions for their organizations and want to demonstrate their expertise in this area.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q16-Q21):

NEW QUESTION # 16
Which copy options are not supported by CREATE PIPE...AS COPY FROM command?

A. FILES = ( 'file_name1' [ , 'file_name2', ... ] )
B. MATCH_BY_COLUMN_NAME = CASE_SENSITIVE | CASE_INSENSITIVE | NONE
C. ON_ERROR = ABORT_STATEMENT
D. FORCE = TRUE | FALSE
E. VALIDATION_MODE = RETURN_n_ROWS | RETURN_ERRORS | RETURN_ALL_ERRORS

Answer: A,B,C,D,E

NEW QUESTION # 17
What are some of the characteristics of result set caches? (Choose three.)

A. Each time persisted results for a query are used, a 24-hour retention period is reset.
B. The retention period can be reset for a maximum of 31 days.
C. The result set cache is not shared between warehouses.
D. Snowflake persists the data results for 24 hours.
E. The data stored in the result cache will contribute to storage costs.
F. Time Travel queries can be executed against the result set cache.

Answer: A,B,D

NEW QUESTION # 18
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 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.
B. 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.
C. 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.
D. 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.

Answer: A

NEW QUESTION # 19
Which Snowflake data modeling approach is designed for BI queries?

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

Answer: B

Explanation:
Explanation
A star schema is a Snowflake data modeling approach that is designed for BI queries. A star schema is a type of dimensional modeling that organizes data into fact tables and dimension tables. A fact table contains the measures or metrics of the business process, such as sales amount, order quantity, or profit margin. A dimension table contains the attributes or descriptors of the business process, such as product name, customer name, or order date. A star schema is called so because it resembles a star, with one fact table in the center and multiple dimension tables radiating from it. A star schema can improve the performance and simplicity of BI queries by reducing the number of joins, providing fast access to aggregated data, and enabling intuitive query syntax. A star schema can also support various types of analysis, such as trend analysis, slice and dice, drill down, and roll up12.
References:
* Snowflake Documentation: Dimensional Modeling
* Snowflake Documentation: Star Schema

NEW QUESTION # 20
You need to choose a high cardinality column for the clustering key

A. TRUE
B. FALSE

Answer: B

NEW QUESTION # 21
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