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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) certification exam is one of the most sought-after certifications in the field of cloud computing. It is a comprehensive exam that assesses the skills and knowledge of professionals in designing, deploying, and managing complex Snowflake environments. SnowPro Advanced Architect Certification certification validates the expertise of an individual in building and maintaining Snowflake data warehouses, data lakes, and data pipelines.

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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.

Passing the Snowflake ARA-C01 Exam demonstrates that a candidate has a deep understanding of Snowflake's architecture and can design and implement complex Snowflake solutions. SnowPro Advanced Architect Certification certification also signifies that the candidate is capable of integrating Snowflake with other technologies and can provide expert guidance to organizations on how to best leverage Snowflake for their specific needs. With the demand for Snowflake expertise on the rise, achieving the ARA-C01 certification can open up new career opportunities and increase earning potential for professionals in the field.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q15-Q20):

NEW QUESTION # 15
You are a snowflake architect in an organization. The business team came to to deploy an use case which requires you to load some data which they can visualize through tableau. Everyday new data comes in and the old data is no longer required.
What type of table you will use in this case to optimize cost

A. TRANSIENT
B. TEMPORARY
C. PERMANENT

Answer: A

Explanation:
* A transient table is a type of table in Snowflake that does 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 replicated1.
* A temporary table is a type of table in Snowflake that is automatically dropped when the session ends or the current user logs out. Temporary tables do not incur any storage costs, but they are not visible to other users or sessions2.
* A permanent table is a type of table in Snowflake that has a Fail-safe period and a Time Travel retention period of up to 90 days. Permanent tables are suitable for persistent and durable data that needs to be protected from accidental or malicious deletion3.
* In this case, the use case requires loading some data that can be visualized through Tableau. The data is updated every day and the old data is no longer required. Therefore, the best type of table to use in this case to optimize cost is a transient table, because it does not incur any Fail-safe costs and it can have a short Time Travel retention period of 0 or 1 day. This way, the data can be loaded and queried by Tableau, and then deleted or overwritten without incurring any unnecessary storage costs.
References: : Transient Tables : Temporary Tables : Understanding & Using Time Travel

NEW QUESTION # 16
The data share exists between a data provider account and a data consumer account. Five tables from the provider account are being shared with the consumer account. The consumer role has been granted the imported privileges privilege.
What will happen to the consumer account if a new table (table_6) is added to the provider schema?

A. The consumer role will automatically see the new table and no additional grants are needed.
B. The consumer role will see the table only after this grant is given on the provider side:
use role accountadmin;
Grant select on table EDW.ACCOUNTING.Table_6 to share PSHARE_EDW_4TEST;
C. The consumer role will see the table only after this grant is given on the provider side:
use role accountadmin;
grant usage on database EDW to share PSHARE_EDW_4TEST ;
grant usage on schema EDW.ACCOUNTING to share PSHARE_EDW_4TEST ;
Grant select on table EDW.ACCOUNTING.Table_6 to database PSHARE_EDW_4TEST_DB ;
D. The consumer role will see the table only after this grant is given on the consumer side:
grant imported privileges on database PSHARE_EDW_4TEST_DB to DEV_ROLE;

Answer: C

Explanation:
When a new table (table_6) is added to a schema in the provider's account that is part of a data share, the consumer will not automatically see the new table. The consumer will only be able to access the new table once the appropriate privileges are granted by the provider. The correct process, as outlined in option D, involves using the provider's ACCOUNTADMIN role to grant USAGE privileges on the database and schema, followed by SELECT privileges on the new table, specifically to the share that includes the consumer's database. This ensures that the consumer account can access the new table under the established data sharing setup.References:
* Snowflake Documentation on Managing Access Control
* Snowflake Documentation on Data Sharing

NEW QUESTION # 17
Consider the following COPY command which is loading data with CSV format into a Snowflake table from an internal stage through a data transformation query.

This command results in the following error:
SQL compilation error: invalid parameter 'validation_mode'
Assuming the syntax is correct, what is the cause of this error?

A. The VALIDATION_MODE parameter does not support COPY statements that transform data during a load.
B. The VALIDATION_MODE parameter does not support COPY statements with CSV file formats.
C. The VALIDATION_MODE parameter supports COPY statements that load data from external stages only.
D. The value return_all_errors of the option VALIDATION_MODE is causing a compilation error.

Answer: A

Explanation:
The VALIDATION_MODE parameter is used to specify the behavior of the COPY statement when loading data into a table. It is used to specify whether the COPY statement should return an error if any of the rows in the file are invalid or if it should continue loading the valid rows. The VALIDATION_MODE parameter is only supported for COPY statements that load data from external stages1.
The query in the question uses a data transformation query to load data from an internal stage. A data transformation query is a query that transforms the data during the load process, such as parsing JSON or XML data, applying functions, or joining with other tables2.
According to the documentation, VALIDATION_MODE does not support COPY statements that transform data during a load. If the parameter is specified, the COPY statement returns an error1. Therefore, option C is the correct answer.

NEW QUESTION # 18
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported.
What could be causing this?

A. There were JSON nulls in the recent data imports.
B. There were variations in string lengths for the JSON values in the recent data imports.
C. The recent data imports contained fewer fields than usual.
D. The order of the keys in the JSON was changed.

Answer: D

Explanation:
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported. This could be caused by the following factors:
* The order of the keys in the JSON was changed. Snowflake stores semi-structured data internally in a column-like structure for the most common elements, and the remainder in a leftovers-like column. The order of the keys in the JSON affects how Snowflake determines the common elements and how it optimizes the query performance. If the order of the keys in the JSON was changed, Snowflake might have to re-parse the data and re-organize the internal storage, which could result in slower query performance.
* There were variations in string lengths for the JSON values in the recent data imports. Non-native values, such as dates and timestamps, are stored as strings when loaded into a VARIANT column.
Operations on these values could be slower and also consume more space than when stored in a relational column with the corresponding data type. If there were variations in string lengths for the JSON values in the recent data imports, Snowflake might have to allocate more space and perform more conversions, which could also result in slower query performance.
The other options are not valid causes for poor query performance:
* There were JSON nulls in the recent data imports. Snowflake supports two types of null values in semi- structured data: SQL NULL and JSON null. SQL NULL means the value is missing or unknown, while JSON null means the value is explicitly set to null. Snowflake can distinguish between these two types of null values and handle them accordingly. Having JSON nulls in the recent data imports should not affect the query performance significantly.
* The recent data imports contained fewer fields than usual. Snowflake can handle semi-structured data with varying schemas and fields. Having fewer fields than usual in the recent data imports should not affect the query performance significantly, as Snowflake can still optimize the data ingestion and query execution based on the existing fields.
Considerations for Semi-structured Data Stored in VARIANT
Snowflake Architect Training
Snowflake query performance on unique element in variant column
Snowflake variant performance

NEW QUESTION # 19
An Architect has designed a data pipeline that Is receiving small CSV files from multiple sources. All of the files are landing in one location. Specific files are filtered for loading into Snowflake tables using the copy command. The loading performance is poor.
What changes can be made to Improve the data loading performance?

A. Create a specific storage landing bucket to avoid file scanning.
B. Increase the size of the virtual warehouse.
C. Create a multi-cluster warehouse and merge smaller files to create bigger files.
D. Change the file format from CSV to JSON.

Answer: C

Explanation:
According to the Snowflake documentation, the data loading performance can be improved by following some best practices and guidelines for preparing and staging the data files. One of the recommendations is to aim for data files that are roughly 100-250 MB (or larger) in size compressed, as this will optimize the number of parallel operations for a load. Smaller files should be aggregated and larger files should be split to achieve this size range. Another recommendation is to use a multi-cluster warehouse for loading, as this will allow for scaling up or out the compute resources depending on the load demand. A single-cluster warehouse may not be able to handle the load concurrency and throughput efficiently. Therefore, by creating a multi-cluster warehouse and merging smaller files to create bigger files, the data loading performance can be improved. Reference:
Data Loading Considerations
Preparing Your Data Files
Planning a Data Load

NEW QUESTION # 20
......

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Valid Snowflake ARA-C01 Valid Exam Camp Pdf Seriously Researched by Snowflake Hard-working Trainers

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Snowflake SnowPro Advanced Architect Certification Sample Questions (Q15-Q20):

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