VALID ARA-C01 Exam Dumps For Certification Exam Preparation [Q10-Q32]

VALID ARA-C01 Exam Dumps For Certification Exam Preparation

ARA-C01 Dumps PDF 2026 Strategy Your Preparation Efficiently

Snowflake ARA-C01: SnowPro Advanced Architect Certification Exam is a highly regarded certification exam in the field of data warehousing and cloud computing. It is designed to test the advanced knowledge and skills of architects who are responsible for designing and implementing complex data warehousing solutions using Snowflake's cloud data platform.

Snowflake ARA-C01 certification exam is suitable for data architects, data engineers, data analysts, and other data professionals who want to demonstrate their advanced knowledge of Snowflake's cloud data platform. It is an excellent way to showcase your expertise to potential employers and clients. SnowPro Advanced Architect Certification certification also provides a competitive advantage in the job market, as it demonstrates that you have a deep understanding of Snowflake's advanced architectural concepts.

 

NEW QUESTION # 10
An Architect is designing a file ingestion recovery solution. The project will use an internal named stage for file storage. Currently, in the case of an ingestion failure, the Operations team must manually download the failed file and check for errors.
Which downloading method should the Architect recommend that requires the LEAST amount of operational overhead?

• A. Use the get command in SnowSQL to retrieve the file.
• B. Use the Snowflake Connector for Python, connect to remote storage and download the file.
• C. Use the get command in Snowsight to retrieve the file.
• D. Use the Snowflake API endpoint and download the file.

Answer: D

NEW QUESTION # 11
Which query will identify the specific days and virtual warehouses that would benefit from a multi-cluster warehouse to improve the performance of a particular workload?

• A.
• B.
• C.
• D.

Answer: A

Explanation:
The correct answer is option B. This query is designed to assess the need for a multi-cluster warehouse by examining the queuing time (AVG_QUEUED_LOAD) on different days and virtual warehouses. When theAVG_QUEUED_LOADis greater than zero, it suggests that queries are waiting for resources, which can be an indicator that performance might be improved by using a multi-cluster warehouse to handle the workload more efficiently. By grouping by date and warehouse name and filtering on the sum of the average queued load being greater than zero, the query identifies specific days and warehouses where the workload exceeded the available compute resources. This information is valuable when considering scaling out warehouses to multi-cluster configurations for improved performance.

NEW QUESTION # 12
What is the MOST efficient way to design an environment where data retention is not considered critical, and customization needs are to be kept to a minimum?

• A. Use a transient database.
• B. Use a transient table.
• C. Use a temporary table.
• D. Use a transient schema.

Answer: A

Explanation:
Transient databases in Snowflake are designed for situations where data retention is not critical, and they do not have the fail-safe period that regular databases have. This means that data in a transient database is not recoverable after the Time Travel retention period. Using a transient database is efficient because it minimizes storage costs while still providing most functionalities of a standard database without the overhead of data protection features that are not needed when data retention is not a concern.

NEW QUESTION # 13
A company is designing a process for importing a large amount of loT JSON data from cloud storage into Snowflake. New sets of loT data get generated and uploaded approximately every 5 minutes.
Once the loT data is in Snowflake, the company needs up-to-date information from an external vendor to join to the data. This data is then presented to users through a dashboard that shows different levels of aggregation. The external vendor is a Snowflake customer.
What solution will MINIMIZE complexity and MAXIMIZE performance?

• A. 1. Create a Snowpipe to bring the JSON data into Snowflake.
  2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives.
  3. Ask the vendor to expose an API so an external function call can be made to join the vendor's data back to the loT data in a transformation procedure.
  4. Create materialized views over the larger dataset to perform the aggregations required by the dashboard.
  5. Give the materialized views access to the dashboard tool.
• B. 1. Create an external table over the JSON data in cloud storage.
  2. Create a task that runs every 5 minutes to run a transformation procedure on new data, based on a saved timestamp.
  3. Ask the vendor to expose an API so an external function can be used to generate a call to join the data back to the loT data in the transformation procedure.
  4. Give the transformed table access to the dashboard tool.
  5. Perform the aggregations on the dashboard tool.
• C. 1. Create a Snowpipe to bring the JSON data into Snowflake.
  2. Use streams and tasks to trigger a transformation procedure when new JSON data arrives.
  3. Ask the vendor to create a data share with the required data that is then imported into the Snowflake account.
  4. Join the vendor's data back to the loT data in a transformation procedure
  5. Create materialized views over the larger dataset to perform the aggregations required by the dashboard.
  6. Give the materialized views access to the dashboard tool.
• D. 1. Create an external table over the JSON data in cloud storage.
  2. Create a task that runs every 5 minutes to run a transformation procedure on new data based on a saved timestamp.
  3. Ask the vendor to create a data share with the required data that can be imported into the company's Snowflake account.
  4. Join the vendor's data back to the loT data using a transformation procedure.
  5. Create views over the larger dataset to perform the aggregations required by the dashboard.
  6. Give the views access to the dashboard tool.

Answer: C

Explanation:
Using Snowpipe for continuous, automated data ingestion minimizes the need for manual intervention and ensures that data is available in Snowflake promptly after it is generated. Leveraging Snowflake's data sharing capabilities allows for efficient and secure access to the vendor's data without the need for complex API integrations. Materialized views provide pre-aggregated data for fast access, which is ideal for dashboards that require high performance1234.
Reference =
* Snowflake Documentation on Snowpipe4
* Snowflake Documentation on Secure Data Sharing2
* Best Practices for Data Ingestion with Snowflake1

NEW QUESTION # 14
How do Snowflake databases that are created from shares differ from standard databases that are not created from shares? (Choose three.)

• A. Shared databases must be refreshed in order for new data to be visible.
• B. Shared databases are read-only.
• C. Shared databases will have the PUBLIC or INFORMATION_SCHEMA schemas without explicitly granting these schemas to the share.
• D. Shared databases are not supported by Time Travel.
• E. Shared databases can also be created as transient databases.
• F. Shared databases cannot be cloned.

Answer: B,D,F

Explanation:
According to the SnowPro Advanced: Architect documents and learning resources, the ways that Snowflake databases that are created from shares differ from standard databases that are not created from shares are:
* Shared databases are read-only. This means that the data consumers who access the shared databases cannot modify or delete the data or the objects in the databases. The data providers who share the databases have full control over the data and the objects, and can grant or revoke privileges on them1.
* Shared databases cannot be cloned. This means that the data consumers who access the shared databases cannot create a copy of the databases or the objects in the databases. The data providers who share the databases can clone the databases or the objects, but the clones are not automatically shared2.
* Shared databases are not supported by Time Travel. This means that the data consumers who access the shared databases cannot use the AS OF clause to query historical data or restore deleted data. The data providers who share the databases can use Time Travel on the databases or the objects, but the historical
* data is not visible to the data consumers3.
The other options are incorrect because they are not ways that Snowflake databases that are created from shares differ from standard databases that are not created from shares. Option B is incorrect because shared databases do not need to be refreshed in order for new data to be visible. The data consumers who access the shared databases can see the latest data as soon as the data providers update the data1. Option E is incorrect because shared databases will not have the PUBLIC or INFORMATION_SCHEMA schemas without explicitly granting these schemas to the share. The data consumers who access the shared databases can only see the objects that the data providers grant to the share, and the PUBLIC and INFORMATION_SCHEMA schemas are not granted by default4. Option F is incorrect because shared databases cannot be created as transient databases. Transient databases are databases that do not support Time Travel or Fail-safe, and can be dropped without affecting the retention period of the data. Shared databases are always created as permanent databases, regardless of the type of the source database5. References: Introduction to Secure Data Sharing | Snowflake Documentation, Cloning Objects | Snowflake Documentation, Time Travel | Snowflake Documentation, Working with Shares | Snowflake Documentation, CREATE DATABASE | Snowflake Documentation

NEW QUESTION # 15
Which data models can be used when modeling tables in a Snowflake environment? (Select THREE).

• A. Data lake
• B. Graph model
• C. lnmon/3NF
• D. Bayesian hierarchical model
• E. Dimensional/Kimball
• F. Data vault

Answer: C,E,F

Explanation:
Snowflake is a cloud data platform that supports various data models for modeling tables in a Snowflake environment. The data models can be classified into two categories: dimensional and normalized.
Dimensional data models are designed to optimize query performance and ease of use for business intelligence and analytics. Normalized data models are designed to reduce data redundancy and ensure data integrity for transactional and operational systems. The following are some of the data models that can be used in Snowflake:
Dimensional/Kimball: This is a popular dimensional data model that uses a star or snowflake schema to organize data into fact and dimension tables. Fact tables store quantitative measures and foreign keys to dimension tables. Dimension tables store descriptive attributes and hierarchies. A star schema has a single denormalized dimension table for each dimension, while a snowflake schema has multiple normalized dimension tables for each dimension. Snowflake supports both star and snowflake schemas, and allows users to create views and joins to simplify queries.
Inmon/3NF: This is a common normalized data model that uses a third normal form (3NF) schema to organize data into entities and relationships. 3NF schema eliminates data duplication and ensures data consistency by applying three rules: 1) every column in a table must depend on the primary key, 2) every column in a table must depend on the whole primary key, not a part of it, and 3) every column in a table must depend only on the primary key, not on other columns. Snowflake supports 3NF schema and allows users to create referential integrity constraints and foreign key relationships to enforce data quality.
Data vault: This is a hybrid data model that combines the best practices of dimensional and normalized data models to create a scalable, flexible, and resilient data warehouse. Data vault schema consists of three types of tables: hubs, links, and satellites. Hubs store business keys and metadata for each entity. Links store associations and relationships between entities. Satellites store descriptive attributes and historical changes for each entity or relationship. Snowflake supports data vault schema and allows users to leverage its features such as time travel, zero-copy cloning, and secure data sharing to implement data vault methodology.
What is Data Modeling? | Snowflake, Snowflake Schema in Data Warehouse Model - GeeksforGeeks, [Data Vault 2.0 Modeling with Snowflake]

NEW QUESTION # 16
A user can change object parameters using which of the following roles?

• A. SECURITYADMIN, USER with PRIVILEGE
• B. SYSADMIN, SECURITYADMIN
• C. ACCOUNTADMIN, SECURITYADMIN
• D. ACCOUNTADMIN, USER with PRIVILEGE

Answer: D

Explanation:
Explanation
According to the Snowflake documentation, object parameters are parameters that can be set on individual objects such as databases, schemas, tables, and stages. Object parameters can be set by users with the appropriate privileges on the objects. For example, to set the object parameter AUTO_REFRESH on a table, the user must have the MODIFY privilege on the table. The ACCOUNTADMIN role has the highest level of privileges on all objects in the account, so it can set any object parameter on any object. However, other roles, such as SECURITYADMIN or SYSADMIN, do not have the same level of privileges on all objects, so they cannot set object parameters on objects they do not own or have the required privileges on. Therefore, the correct answer is C. ACCOUNTADMIN, USER with PRIVILEGE.
References:
* Parameters | Snowflake Documentation
* Object Parameters | Snowflake Documentation
* Object Privileges | Snowflake Documentation

NEW QUESTION # 17
When using the Snowflake Connector for Kafka, what data formats are supported for the messages? (Choose two.)

• A. CSV
• B. Parquet
• C. JSON
• D. Avro
• E. XML

Answer: C,D

Explanation:
Explanation
The data formats that are supported for the messages when using the Snowflake Connector for Kafka are Avro and JSON. These are the two formats that the connector can parse and convert into Snowflake table rows. The connector supports both schemaless and schematized JSON, as well as Avro with or without a schema registry1. The other options are incorrect because they are not supported data formats for the messages. CSV, XML, and Parquet are not formats that the connector can parse and convert into Snowflake table rows. If the messages are in these formats, the connector will load them as VARIANT data type and store them as raw strings in the table2. References: Snowflake Connector for Kafka | Snowflake Documentation, Loading Protobuf Data using the Snowflake Connector for Kafka | Snowflake Documentation

NEW QUESTION # 18
Role A has the following permissions:
. USAGE on db1
. USAGE and CREATE VIEW on schemal in db1
. SELECT on tablel in schemal
Role B has the following permissions:
. USAGE on db2
. USAGE and CREATE VIEW on schema2 in db2
. SELECT on table2 in schema2
A user has Role A set as the primary role and Role B as a secondary role.
What command will fail for this user?

• A. use database db2;
  use schema schema2;
  create view v2 as select * from dbl.schemal. tablel;
• B. use database db1;
  use schema schemal;
  create view v1 as select * from db2.schema2.table2;
• C. use database db2;
  use schema schema2;
  select * from db1.schemal.tablel union select * from table2;
• D. use database db1;
  use schema schemal;
  select * from db2.schema2.table2;

Answer: A

NEW QUESTION # 19
Which steps are recommended best practices for prioritizing cluster keys in Snowflake? (Choose two.)

• A. Choose columns that are frequently used in join predicates.
• B. Choose TIMESTAMP columns with nanoseconds for the highest number of unique rows.
• C. Choose lower cardinality columns to support clustering keys and cost effectiveness.
• D. Choose cluster columns that are most actively used in selective filters.
• E. Choose cluster columns that are actively used in the GROUP BY clauses.

Answer: A,D

Explanation:
According to the Snowflake documentation, the best practices for choosing clustering keys are:
* Choose columns that are frequently used in join predicates. This can improve the join performance by reducing the number of micro-partitions that need to be scanned and joined.
* Choose columns that are most actively used in selective filters. This can improve the scan efficiency by skipping micro-partitions that do not match the filter predicates.
* Avoid using low cardinality columns, such as gender or country, as clustering keys. This can result in poor clustering and high maintenance costs.
* Avoid using TIMESTAMP columns with nanoseconds, as they tend to have very high cardinality and low correlation with other columns. This can also result in poor clustering and high maintenance costs.
* Avoid using columns with duplicate values or NULLs, as they can cause skew in the clustering and reduce the benefits of pruning.
* Cluster on multiple columns if the queries use multiple filters or join predicates. This can increase the chances of pruning more micro-partitions and improve the compression ratio.
* Clustering is not always useful, especially for small or medium-sized tables, or tables that are not frequently queried or updated. Clustering can incur additional costs for initially clustering the data and maintaining the clustering over time.
Clustering Keys & Clustered Tables | Snowflake Documentation
[Considerations for Choosing Clustering for a Table | Snowflake Documentation]

NEW QUESTION # 20
Every Snowflake table loaded by the Kafka connector has a schema consisting of two VARIANT columns.
Which are those?

• A. RECORD_CONTENT
• B. RECORD_MESSAGE
• C. RECORD_METADATA

Answer: A,C

NEW QUESTION # 21
An Architect for a multi-national transportation company has a system that is used to check the weather conditions along vehicle routes. The data is provided to drivers.
The weather information is delivered regularly by a third-party company and this information is generated as JSON structure. Then the data is loaded into Snowflake in a column with a VARIANT data type. This table is directly queried to deliver the statistics to the drivers with minimum time lapse.
A single entry includes (but is not limited to):
- Weather condition; cloudy, sunny, rainy, etc.
- Degree
- Longitude and latitude
- Timeframe
- Location address
- Wind
The table holds more than 10 years' worth of data in order to deliver the statistics from different years and locations. The amount of data on the table increases every day.
The drivers report that they are not receiving the weather statistics for their locations in time.
What can the Architect do to deliver the statistics to the drivers faster?

• A. Divide the table into several tables for each year by using the timeframe information from the JSON dataset in order to process the queries in parallel.
• B. Add search optimization service on the variant column for longitude and latitude in order to query the information by using specific metadata.
• C. Divide the table into several tables for each location by using the location address information from the JSON dataset in order to process the queries in parallel.
• D. Create an additional table in the schema for longitude and latitude. Determine a regular task to fill this information by extracting it from the JSON dataset.

Answer: B

Explanation:
To improve the performance of queries on semi-structured data, such as JSON stored in a VARIANT column, Snowflake's search optimization service can be utilized. By adding search optimization specifically for the longitude and latitude fields within the VARIANT column, the system can perform point lookups and substring queries more efficiently. This will allow for faster retrieval of weather statistics, which is critical for the drivers to receive timely updates.

NEW QUESTION # 22
Consider the following scenario where a masking policy is applied on the CREDICARDND column of the CREDITCARDINFO table. The masking policy definition Is as follows:

Sample data for the CREDITCARDINFO table is as follows:
NAME EXPIRYDATE CREDITCARDNO
JOHN DOE 2022-07-23 4321 5678 9012 1234
if the Snowflake system rotes have not been granted any additional roles, what will be the result?

• A. The sysadmin can see the CREDICARDND column data in clear text.
• B. Anyone with the Pl_ANALYTICS role will see the last 4 characters of the CREDICARDND column data in dear text.
• C. The owner of the table will see the CREDICARDND column data in clear text.
• D. Anyone with the Pl_ANALYTICS role will see the CREDICARDND column as*** 'MASKED* **'.

Answer: D

Explanation:
* The masking policy defined in the image indicates that if a user has the PI_ANALYTICS role, they will be able to see the last 4 characters of the CREDITCARDNO column data in clear text. Otherwise, they will see 'MASKED'. Since Snowflake system roles have not been granted any additional roles, they won't have the PI_ANALYTICS role and therefore cannot view the last 4 characters of credit card numbers.
* To apply a masking policy on a column in Snowflake, you need to use the ALTER TABLE ... ALTER COLUMN command or the ALTER VIEW command and specify the policy name. For example, to apply the creditcardno_mask policy on the CREDITCARDNO column of the CREDITCARDINFO table, you can use the following command:
ALTER TABLE CREDITCARDINFO ALTER COLUMN CREDITCARDNO SET MASKING POLICY creditcardno_mask;
* For more information on how to create and use masking policies in Snowflake, you can refer to the following resources:
CREATE MASKING POLICY: This document explains the syntax and usage of the CREATE MASKING POLICY command, which allows you to create a new masking policy or replace an existing one.
Using Dynamic Data Masking: This guide provides instructions on how to configure and use dynamic data masking in Snowflake, which is a feature that allows you to mask sensitive data based on the execution context of the user.
ALTER MASKING POLICY: This document explains the syntax and usage of the ALTER MASKING POLICY command, which allows you to modify the properties of an existing masking policy.
References: 1: https://docs.snowflake.com/en/sql-reference/sql/create-masking-policy 2:
https://docs.snowflake.com/en/user-guide/security-column-ddm-use 3:
https://docs.snowflake.com/en/sql-reference/sql/alter-masking-policy

NEW QUESTION # 23
When loading data into a table that captures the load time in a column with a default value of either CURRENT_TIME () or CURRENT_TIMESTAMP() what will occur?

• A. Any rows loaded using a specific COPY statement will have varying timestamps based on when the rows were created in the source.
• B. All rows loaded using a specific COPY statement will have the same timestamp value.
• C. Any rows loaded using a specific COPY statement will have varying timestamps based on when the rows were read from the source.
• D. All rows loaded using a specific COPY statement will have varying timestamps based on when the rows were inserted.

Answer: B

Explanation:
According to the Snowflake documentation, when loading data into a table that captures the load time in a column with a default value of either CURRENT_TIME () or CURRENT_TIMESTAMP(), the default value is evaluated once per COPY statement, not once per row. Therefore, all rows loaded using a specific COPY statement will have the same timestamp value. This behavior ensures that the timestamp value reflects the time when the data was loaded into the table, not when the data was read from the source or created in the source.
References:
* Snowflake Documentation: Loading Data into Tables with Default Values
* Snowflake Documentation: COPY INTO table

NEW QUESTION # 24
A retail company has 2000+ stores spread across the country. Store Managers report that they are having trouble running key reports related to inventory management, sales targets, payroll, and staffing during business hours. The Managers report that performance is poor and time-outs occur frequently.
Currently all reports share the same Snowflake virtual warehouse.
How should this situation be addressed? (Select TWO).

• A. Configure a dedicated virtual warehouse for the Store Manager team.
• B. Use a Business Intelligence tool for in-memory computation to improve performance.
• C. Advise the Store Manager team to defer report execution to off-business hours.
• D. Configure the virtual warehouse to be multi-clustered.
• E. Configure the virtual warehouse to size 4-XL

Answer: A,D

Explanation:
The best way to address the performance issues and time-outs faced by the Store Manager team is to configure a dedicated virtual warehouse for them and make it multi-clustered. This will allow them to run their reports independently from other workloads and scale up or down the compute resources as needed. A dedicated virtual warehouse will also enable them to apply specific security and access policies for their data. A multi-clustered virtual warehouse will provide high availability and concurrency for their queries and avoid queuing or throttling.
Using a Business Intelligence tool for in-memory computation may improve performance, but it will not solve the underlying issue of insufficient compute resources in the shared virtual warehouse. It will also introduce additional costs and complexity for the data architecture.
Configuring the virtual warehouse to size 4-XL may increase the performance, but it will also increase the cost and may not be optimal for the workload. It will also not address the concurrency and availability issues that may arise from sharing the virtual warehouse with other workloads.
Advising the Store Manager team to defer report execution to off-business hours may reduce the load on the shared virtual warehouse, but it will also reduce the timeliness and usefulness of the reports for the business. It will also not guarantee that the performance issues and time-outs will not occur at other times.
Reference:
Snowflake Architect Training
Snowflake SnowPro Advanced Architect Certification - Preparation Guide
SnowPro Advanced: Architect Exam Study Guide

NEW QUESTION # 25
The following DDL command was used to create a task based on a stream:

Assuming MY_WH is set to auto_suspend - 60 and used exclusively for this task, which statement is true?

• A. The warehouse MY_WH will be made active every five minutes to check the stream.
• B. The warehouse MY_WH will never suspend.
• C. The warehouse MY_WH will automatically resize to accommodate the size of the stream.
• D. The warehouse MY_WH will only be active when there are results in the stream.

Answer: A

NEW QUESTION # 26
The Data Engineering team at a large manufacturing company needs to engineer data coming from many sources to support a wide variety of use cases and data consumer requirements which include:
1) Finance and Vendor Management team members who require reporting and visualization
2) Data Science team members who require access to raw data for ML model development
3) Sales team members who require engineered and protected data for data monetization What Snowflake data modeling approaches will meet these requirements? (Choose two.)

• A. Consolidate data in the company's data lake and use EXTERNAL TABLES.
• B. Create a set of profile-specific databases that aligns data with usage patterns.
• C. Create a single star schema in a single database to support all consumers' requirements.
• D. Create a raw database for landing and persisting raw data entering the data pipelines.
• E. Create a Data Vault as the sole data pipeline endpoint and have all consumers directly access the Vault.

Answer: C,E

NEW QUESTION # 27
A company's client application supports multiple authentication methods, and is using Okta.
What is the best practice recommendation for the order of priority when applications authenticate to Snowflake?

• A. 1) Okta native authentication2) Key Pair Authentication, mostly used for production environment users3) Password4) OAuth (either Snowflake OAuth or External OAuth)5) External browser, SSO
• B. 1) Password2) Key Pair Authentication, mostly used for production environment users3) Okta native authentication4) OAuth (either Snowflake OAuth or External OAuth)5) External browser, SSO
• C. 1) External browser, SSO2) Key Pair Authentication, mostly used for development environment users3) Okta native authentication4) OAuth (ether Snowflake OAuth or External OAuth)5) Password
• D. 1) OAuth (either Snowflake OAuth or External OAuth)2) External browser3) Okta native authentication4) Key Pair Authentication, mostly used for service account users5) Password

Answer: D

Explanation:
This is the best practice recommendation for the order of priority when applications authenticate to Snowflake, according to the Snowflake documentation and the web search results. Authentication is the process of verifying the identity of a user or application that connects to Snowflake. Snowflake supports multiple authentication methods, each with different advantages and disadvantages. The recommended order of priority is based on the following factors:
Security: The authentication method should provide a high level of security and protection against unauthorized access or data breaches. The authentication method should also support multi-factor authentication (MFA) or single sign-on (SSO) for additional security.
Convenience: The authentication method should provide a smooth and easy user experience, without requiring complex or manual steps. The authentication method should also support seamless integration with external identity providers or applications.
Flexibility: The authentication method should provide a range of options and features to suit different use cases and scenarios. The authentication method should also support customization and configuration to meet specific requirements.
Based on these factors, the recommended order of priority is:
OAuth (either Snowflake OAuth or External OAuth): OAuth is an open standard for authorization that allows applications to access Snowflake resources on behalf of a user, without exposing the user's credentials.
OAuth provides a high level of security, convenience, and flexibility, as it supports MFA, SSO, token-based authentication, and various grant types and scopes. OAuth can be implemented using either Snowflake OAuth or External OAuth, depending on the identity provider and the application12.
External browser: External browser is an authentication method that allows users to log in to Snowflake using a web browser and an external identity provider, such as Okta, Azure AD, or Ping Identity. External browser provides a high level of security and convenience, as it supports MFA, SSO, and federated authentication. External browser also provides a consistent user interface and experience across different platforms and devices34.
Okta native authentication: Okta native authentication is an authentication method that allows users to log in to Snowflake using Okta as the identity provider, without using a web browser. Okta native authentication provides a high level of security and convenience, as it supports MFA, SSO, and federated authentication. Okta native authentication also provides a native user interface and experience for Okta users, and supports various Okta features, such as password policies and user management56.
Key Pair Authentication: Key Pair Authentication is an authentication method that allows users to log in to Snowflake using a public-private key pair, without using a password. Key Pair Authentication provides a high level of security, as it relies on asymmetric encryption and digital signatures. Key Pair Authentication also provides a flexible and customizable authentication option, as it supports various key formats, algorithms, and expiration times. Key Pair Authentication is mostly used for service account users, such as applications or scripts that connect to Snowflake programmatically7 .
Password: Password is the simplest and most basic authentication method that allows users to log in to Snowflake using a username and password. Password provides a low level of security, as it relies on symmetric encryption and is vulnerable to brute force attacks or phishing. Password also provides a low level of convenience and flexibility, as it requires manual input and management, and does not support MFA or SSO. Password is the least recommended authentication method, and should be used only as a last resort or for testing purposes .
Snowflake Documentation: Snowflake OAuth
Snowflake Documentation: External OAuth
Snowflake Documentation: External Browser Authentication
Snowflake Blog: How to Use External Browser Authentication with Snowflake Snowflake Documentation: Okta Native Authentication Snowflake Blog: How to Use Okta Native Authentication with Snowflake Snowflake Documentation: Key Pair Authentication
[Snowflake Blog: How to Use Key Pair Authentication with Snowflake]
[Snowflake Documentation: Password Authentication]
[Snowflake Blog: How to Use Password Authentication with Snowflake]

NEW QUESTION # 28
As of today snowflake supports replication for databases only

• A. FALSE
• B. TRUE

Answer: B

NEW QUESTION # 29
An Architect is troubleshooting a long-running statement and needs to identify blocked transactions and the queries blocking them.
Which views should be used? (Select TWO).

• A. ACCESS_HISTORY
• B. OBJECT_DEPENDENCIES
• C. QUERY_HISTORY
• D. LOCK_WAIT_HISTORY
• E. DATA_TRANSFER_HISTORY

Answer: C,D

Explanation:
LOCK_WAIT_HISTORY provides detailed information about transactions waiting on locks, including which transactions are blocked and which ones are blocking them (Answer D). This view is essential for diagnosing contention and concurrency issues.
QUERY_HISTORY complements this by providing execution details about the blocking queries, such as duration, user, and SQL text (Answer A). Together, these views allow architects to correlate blocked transactions with the responsible workloads.
The other views are unrelated to transaction locking behavior. This question highlights SnowPro Architect troubleshooting skills related to concurrency and transaction management.
=========

NEW QUESTION # 30
You want to automatically delete the files from stage after a successful load using the COPY INTO command.
What will be recommended approach for deletion?

• A. Set PURGE=TRUE in the COPY INTO command
• B. Set REMOVE=TRUE in the COPY INTO Command
• C. No need to do anything, snowflake does it automatically

Answer: A

NEW QUESTION # 31
A Snowflake Architect is designing an application and tenancy strategy for an organization where strong legal isolation rules as well as multi-tenancy are requirements.
Which approach will meet these requirements if Role-Based Access Policies (RBAC) is a viable option for isolating tenants?

• A. Create a multi-tenant table strategy if row level security is not viable for isolating tenants.
• B. Create an object for each tenant strategy if row level security is viable for isolating tenants.
• C. Create accounts for each tenant in the Snowflake organization.
• D. Create an object for each tenant strategy if row level security is not viable for isolating tenants.

Answer: B

NEW QUESTION # 32
......

Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a cloud-based certification exam that is designed to validate the advanced skills and knowledge of Snowflake architects. SnowPro Advanced Architect Certification certification exam is intended for those professionals who possess a deep understanding of Snowflake data warehouses and their architecture, and who can design and implement complex Snowflake solutions using best practices. The SnowPro Advanced Architect Certification Exam is a vendor-neutral certification, which means that it is not affiliated with any particular vendor or technology.

 

Latest Verified & Correct ARA-C01 Questions: https://www.easy4engine.com/ARA-C01-test-engine.html

100% Pass Guaranteed Download SnowPro Advanced Certification Exam PDF Q&A: https://drive.google.com/open?id=1y9nRFFS6xEB0V1YoWq0hg4gGj2t4SdnM