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Snowflake ARA-C01 Exam Syllabus Topics:

Section	Weight	Objectives
Performance Optimization	20%	- Clustering and partitioning strategies - Cost optimization and resource management - Query performance tuning - Virtual warehouse configuration and sizing
Data Engineering	25%	- Data loading and unloading solutions - Transformation and processing frameworks - Data pipeline and integration design
Snowflake Architecture	30%	- Development lifecycle and workload support - Data protection and recovery strategies - Object hierarchy and architectural impact - Data sharing architecture design - Data modeling and use cases
Accounts and Security	25%	- Security, compliance and governance architecture - Security principles and implementation scenarios - Account and database strategy design

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Echte ARA-C01 Fragen und Antworten der ARA-C01 Zertifizierungsprüfung

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Snowflake SnowPro Advanced Architect Certification ARA-C01 Prüfungsfragen mit Lösungen (Q155-Q160):

155. Frage
Which alter command below may affect the availability of column with respect to time travel?

A. ALTER TABLE...SET DEFAULT
B. ALTER TABLE...SET DATA TYPE
C. ALTER TABLE...DROP COLUMN

Antwort: B

156. Frage
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 ang 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. 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.
B. 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.
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 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.

Antwort: B

Begründung:
This design meets all the requirements for the data pipeline. Snowpipe is a feature that enables continuous data loading into Snowflake from object storage using event notifications. It is efficient, scalable, and serverless, meaning it does not require any infrastructure or maintenance from the user. Streams and tasks are features that enable automated data pipelines within Snowflake, using change data capture and scheduled execution. They are also efficient, scalable, and serverless, and they simplify the data transformation process.
External functions are functions that can invoke external services or APIs from within Snowflake. They can be used to integrate with Amazon Comprehend and perform sentiment analysis on the data. The results can be written back to a Snowflake table using standard SQL commands. Snowflake Marketplace is a platform that allows data providers to share data with data consumers across different accounts, regions, and cloud platforms. It is a secure and easy way to make data publicly available to other companies.
Snowpipe Overview | Snowflake Documentation
Introduction to Data Pipelines | Snowflake Documentation
External Functions Overview | Snowflake Documentation
Snowflake Data Marketplace Overview | Snowflake Documentation

157. Frage
The IT Security team has identified that there is an ongoing credential stuffing attack on many of their organization's system.
What is the BEST way to find recent and ongoing login attempts to Snowflake?

A. Query the LOGIN_HISTORY view in the ACCOUNT_USAGE schema in the SNOWFLAKE database.
B. View the Users section in the Account tab in the Snowflake UI and review the last login column.
C. Call the LOGIN_HISTORY Information Schema table function.
D. View the History tab in the Snowflake UI and set up a filter for SQL text that contains the text "LOGIN".

Antwort: A

Begründung:
This view can be used to query login attempts by Snowflake users within the last 365 days (1 year). It provides information such as the event timestamp, the user name, the client IP, the authentication method, the success or failure status, and the error code or message if the login attempt was unsuccessful. By querying this view, the IT Security team can identify any suspicious or malicious login attempts to Snowflake and take appropriate actions to prevent credential stuffing attacks1. The other options are not the best ways to find recent and ongoing login attempts to Snowflake. Option A is incorrect because the LOGIN_HISTORY Information Schema table function only returns login events within the last 7 days, which may not be sufficient to detect credential stuffing attacks that span a longer period of time2. Option C is incorrect because the History tab in the Snowflake UI only shows the queries executed by the current user or role, not the login events of other users or roles3. Option D is incorrect because the Users section in the Account tab in the Snowflake UI only shows the last login time for each user, not the details of the login attempts or the failures.

158. Frage
When would you usually consider to add clustering key to a table

A. The number of users querying the table has increased
B. The table has more than 20 columns
C. it is a multi-terabyte size table
D. The performance of the query has deteriorated over a period of time.

Antwort: C,D

159. Frage
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. Change the file format from CSV to JSON.
C. Create a multi-cluster warehouse and merge smaller files to create bigger files.
D. Increase the size of the virtual warehouse.

Antwort: C

160. Frage
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