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ARA-C01 Actual Exam Preparation Materials and ARA-C01 Test Engine - PDFVCE

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Snowflake ARA-C01 exam is a challenging exam that requires a significant amount of preparation and study. Candidates should have experience working with Snowflake and should be familiar with Snowflake best practices and implementation strategies. ARA-C01 exam consists of multiple-choice questions and scenario-based questions that test the candidate's ability to design and implement Snowflake solutions. Candidates who pass the exam will earn the SnowPro Advanced Architect certification, which is a highly respected certification in the data warehousing industry.

Snowflake ARA-C01 certification exam consists of 60 multiple-choice questions and has a time limit of 120 minutes. ARA-C01 exam is administered online and can be taken from anywhere in the world. The passing score for the exam is 80%, and individuals who pass the exam will receive the SnowPro Advanced Architect Certification. SnowPro Advanced Architect Certification certification is recognized globally and demonstrates that the holder has the skills and knowledge to design and deploy complex Snowflake solutions that meet the needs of their organization. The SnowPro Advanced Architect Certification is a valuable credential for data architects, data engineers, and data analysts who work with Snowflake and want to advance their careers in the field of data management and analytics.

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.

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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q130-Q135):

NEW QUESTION # 130
You have a need to make external file data available to your users with the lowest latency. The files are on an external stage in AWS.
What feature of Snowflake is the most appropriate to use

A. Secure View
B. SnowPipe
C. Materialized View

Answer: B

NEW QUESTION # 131
Following objects can be cloned in snowflake

A. Transient table
B. Temporary table
C. Internal stages
D. Permanent table
E. External tables

Answer: A,B,D

NEW QUESTION # 132
Which command below will load data from result_scan to a table?

A. CREATE OR REPLACE TABLE STORE_FROM_RESULT_SCAN AS select * from table(result_scan(last_query_id()));
B. CREATE OR REPLACE TABLE STORE_FROM_RESULT_SCAN AS select * from result_scan(last_query_id());
C. INSERT INTO STORE_FROM_RESULT_SCAN select * from result_scan(last_query_id());

Answer: A

NEW QUESTION # 133
Which columns can be included in an external table schema? (Select THREE).

A. METADATASROW_ID
B. METADATASISUPDATE
C. VALUE
D. METADAT A$ FILENAME
E. METADATAS FILE_ROW_NUMBER
F. METADATASEXTERNAL TABLE PARTITION

Answer: C,D,E

Explanation:
An external table schema defines the columns and data types of the data stored in an external stage. All external tables include the following columns by default:
* VALUE: A VARIANT type column that represents a single row in the external file.
* METADATA$FILENAME: A pseudocolumn that identifies the name of each staged data file included in the external table, including its path in the stage.
* METADATA$FILE_ROW_NUMBER: A pseudocolumn that shows the row number for each record in a staged data file.
You can also create additional virtual columns as expressions using the VALUE column and/or the pseudocolumns. However, the following columns are not valid for external tables and cannot be included in the schema:
* METADATASROW_ID: This column is only available for internal tables and shows the unique identifier for each row in the table.
* METADATASISUPDATE: This column is only available for internal tables and shows whether the row was inserted or updated by a merge operation.
* METADATASEXTERNAL TABLE PARTITION: This column is not a valid column name and does not exist in Snowflake.
References: Introduction to External Tables, CREATE EXTERNAL TABLE

NEW QUESTION # 134
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 query is running slow because the warehouse does not have enough memory
B. The query is running slow because BIBNUMBER does not have an index created on it.
C. 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

Answer: C

NEW QUESTION # 135
......

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