COMPOSITE TEST ARA-C01 PRICE - ARA-C01 LATEST EXAM GUIDE

Composite Test ARA-C01 Price - ARA-C01 Latest Exam Guide

Composite Test ARA-C01 Price - ARA-C01 Latest Exam Guide

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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 ARA-C01 Certification Exam is a computer-based test. It consists of 95 multiple-choice questions that must be completed in 120 minutes. ARA-C01 exam is administered by Pearson VUE, a leading provider of certification exams globally. Candidates can take the exam at any authorized Pearson VUE testing center, making it convenient for professionals worldwide.

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To become certified in Snowflake ARA-C01, candidates must first meet the eligibility requirements, which include having experience in data warehousing, data modeling, and data integration. Candidates must also complete the SnowPro Core Certification Exam and pass the SnowPro Advanced Architect Certification Exam, which is a rigorous and comprehensive exam that tests advanced knowledge and skills in Snowflake architecture.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q88-Q93):

NEW QUESTION # 88
Which of the following are characteristics of Snowflake's parameter hierarchy?

  • A. Schema parameters override account parameters.
  • B. Session parameters override virtual warehouse parameters.
  • C. Table parameters override virtual warehouse parameters.
  • D. Virtual warehouse parameters override user parameters.

Answer: A

Explanation:
This is the correct answer because it reflects the characteristics of Snowflake's parameter hierarchy.
Snowflake provides three types of parameters that can be set for an account: account parameters, session parameters, and object parameters. All parameters have default values, which can be set and then overridden at different levels depending on the parameter type. The following diagram illustrates the hierarchical relationship between the different parameter types and how individual parameters can be overridden at each level1:
As shown in the diagram, schema parameters are a type of object parameters that can be set for schemas.
Schema parameters can override the account parameters that are set at the account level. For example, the LOG_LEVEL parameter can be set at the account level to control the logging level for all objects in the account, but it can also be overridden at the schema level to control the logging level for specific stored procedures and UDFs in that schema2.
The other options listed are not correct because they do not reflect the characteristics of Snowflake's parameter hierarchy. Session parameters do not override virtual warehouse parameters, because virtual warehouse parameters are a type of session parameters that can be set for virtual warehouses. Virtual warehouse parameters do not override user parameters, because user parameters are a type of session parameters that can be set for users. Table parameters do not override virtual warehouse parameters, because table parameters are a type of object parameters that can be set for tables, and object parameters do not affect session parameters1.
References:
* Snowflake Documentation: Parameters
* Snowflake Documentation: Setting Log Level


NEW QUESTION # 89
A company is using a Snowflake account in Azure. The account has SAML SSO set up using ADFS as a SCIM identity provider. To validate Private Link connectivity, an Architect performed the following steps:
* Confirmed Private Link URLs are working by logging in with a username/password account
* Verified DNS resolution by running nslookups against Private Link URLs
* Validated connectivity using SnowCD
* Disabled public access using a network policy set to use the company's IP address range However, the following error message is received when using SSO to log into the company account:
IP XX.XXX.XX.XX is not allowed to access snowflake. Contact your local security administrator.
What steps should the Architect take to resolve this error and ensure that the account is accessed using only Private Link? (Choose two.)

  • A. Generate a new SCIM access token using system$generate_scim_access_token and save it to Azure AD.
  • B. Open a case with Snowflake Support to authorize the Private Link URLs' access to the account.
  • C. Update the configuration of the Azure AD SSO to use the Private Link URLs.
  • D. Alter the Azure security integration to use the Private Link URLs.
  • E. Add the IP address in the error message to the allowed list in the network policy.

Answer: C,E

Explanation:
The error message indicates that the IP address in the error message is not allowed to access Snowflake because it is not in the allowed list of the network policy. The network policy is a feature that allows restricting access to Snowflake based on IP addresses or ranges. To resolve this error, the Architect should take the following steps:
Add the IP address in the error message to the allowed list in the network policy. This will allow the IP address to access Snowflake using the Private Link URLs. Alternatively, the Architect can disable the network policy if it is not required for security reasons.
Update the configuration of the Azure AD SSO to use the Private Link URLs. This will ensure that the SSO authentication process uses the Private Link URLs instead of the public URLs. The configuration can be updated by following the steps in the Azure documentation1.
These two steps should resolve the error and ensure that the account is accessed using only Private Link. The other options are not necessary or relevant for this scenario. Altering the Azure security integration to use the Private Link URLs is not required because the security integration is used for SCIM provisioning, not for SSO authentication. Generating a new SCIM access token using system$generate_scim_access_token and saving it to Azure AD is not required because the SCIM access token is used for SCIM provisioning, not for SSO authentication. Opening a case with Snowflake Support to authorize the Private Link URLs' access to the account is not required because the authorization can be done by the account administrator using the SYSTEM$AUTHORIZE_PRIVATELINK function2.


NEW QUESTION # 90
An Architect on a new project has been asked to design an architecture that meets Snowflake security, compliance, and governance requirements as follows:
1) Use Tri-Secret Secure in Snowflake
2) Share some information stored in a view with another Snowflake customer
3) Hide portions of sensitive information from some columns
4) Use zero-copy cloning to refresh the non-production environment from the production environment To meet these requirements, which design elements must be implemented? (Choose three.)

  • A. Use the Enterprise edition of Snowflake.
  • B. Create a materialized view.
  • C. Define row access policies.
  • D. Use Dynamic Data Masking.
  • E. Create a secure view.
  • F. Use theBusiness-Criticaledition of Snowflake.

Answer: D,E,F

Explanation:
Explanation
These three design elements are required to meet the security, compliance, and governance requirements for the project.
* To use Tri-Secret Secure in Snowflake, the Business Critical edition of Snowflake is required. This edition provides enhanced data protection features, such as customer-managed encryption keys, that are not available in lower editions. Tri-Secret Secure is a feature that combines a Snowflake-maintained key and a customer-managed key to create a composite master key to encrypt the data in Snowflake1.
* To share some information stored in a view with another Snowflake customer, a secure view is recommended. A secure view is a view that hides the underlying data and the view definition from unauthorized users. Only the owner of the view and the users who are granted the owner's role can see the view definition and the data in the base tables of the view2. A secure view can be shared with another Snowflake account using a data share3.
* To hide portions of sensitive information from some columns, Dynamic Data Masking can be used.
Dynamic Data Masking is a feature that allows applying masking policies to columns to selectively mask plain-text data at query time. Depending on the masking policy conditions and the user's role, the data can be fully or partially masked, or shown as plain-text4.


NEW QUESTION # 91
Which of the following ingestion methods can be used to load near real-time data by using the messaging services provided by a cloud provider?

  • A. Snowflake streams
  • B. Snowpipe
  • C. Snowflake Connector for Kafka
  • D. Spark

Answer: B,C

Explanation:
Snowflake Connector for Kafka and Snowpipe are two ingestion methods that can be used to load near real-time data by using the messaging services provided by a cloud provider. Snowflake Connector for Kafka enables you to stream structured and semi-structured data from Apache Kafka topics into Snowflake tables.
Snowpipe enables you to load data from files that are continuously added to a cloud storage location, such as Amazon S3 or Azure Blob Storage. Both methods leverage Snowflake's micro-partitioning and columnar storage to optimize data ingestion and query performance. Snowflake streams and Spark are not ingestion methods, but rather components of the Snowflake architecture. Snowflake streams provide change data capture (CDC) functionality by tracking data changes in a table. Spark is a distributed computing framework that can be used to process large-scale data and write it to Snowflake using the Snowflake Spark Connector. References:
* Snowflake Connector for Kafka
* Snowpipe
* Snowflake Streams
* Snowflake Spark Connector


NEW QUESTION # 92
An Architect runs the following SQL query:

How can this query be interpreted?

  • A. FILEROWS is a file. FILE_ROW_NUMBER is the file format location.
  • B. FILEROWS is a stage. FILE_ROW_NUMBER is line number in file.
  • C. FILEROWS is the table. FILE_ROW_NUMBER is the line number in the table.
  • D. FILERONS is the file format location. FILE_ROW_NUMBER is a stage.

Answer: B

Explanation:
* A stage is a named location in Snowflake that can store files for data loading and unloading. A stage can be internal or external, depending on where the files are stored.
* The query in the question uses the LIST function to list the files in a stage named FILEROWS. The function returns a table with various columns, including FILE_ROW_NUMBER, which is the line number of the file in the stage.
* Therefore, the query can be interpreted as listing the files in a stage named FILEROWS and showing the line number of each file in the stage.
Stages
LIST Function


NEW QUESTION # 93
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