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Snowflake ARA-R01 Exam Questions
- Topic 1: Accounts and Security: This section relates to creating a Snowflake account and a database strategy aligned with business needs. Users are tested for developing an architecture that satisfies data security, privacy, compliance, and governance standards.
- Topic 2: Snowflake Architecture: This section assesses examining the advantages and constraints of different data models, devises data-sharing strategies, and developing architectural solutions that accommodate Development Lifecycles and workload needs.
- Topic 3: Data Engineering: This section is about identifying the optimal data loading or unloading method to fulfill business requirements. Examine the primary tools within Snowflake's ecosystem and their integration with the platform.
- Topic 4: Performance Optimization: This section is about summarizing performance tools, recommended practices, and their ideal application scenarios, addressing performance challenges within current architectures, and resolving them.
Free Snowflake ARA-R01 Exam Actual Questions
Note: Premium Questions for ARA-R01 were last updated On Oct. 30, 2026 (see below)
A company needs to share its product catalog data with one of its partners. The product catalog data is stored in two database tables: product_category, and product_details. Both tables can be joined by the product_id column. Data access should be governed, and only the partner should have access to the records.
The partner is not a Snowflake customer. The partner uses Amazon S3 for cloud storage.
Which design will be the MOST cost-effective and secure, while using the required Snowflake features?
A reader account is a type of Snowflake account that allows external users to access data shared by a provider account without being a Snowflake customer. A reader account can be created and managed by the provider account, and can use the Snowflake web interface or JDBC/ODBC drivers to query the shared data.A reader account is billed to the provider account based on the credits consumed by the queries1. A secure view is a type of view that applies row-level security filters to the underlying tables, and masks the data that is not accessible to the user.A secure view can be shared with a reader account to provide granular and governed access to the data2. In this scenario, creating a reader account for the partner and sharing the data sets as secure views would be the most cost-effective and secure design, while using the required Snowflake features, because:
It would avoid the data transfer and storage costs of using an S3 bucket as a destination, and the potential security risks of exposing the data to unauthorized access or modification.
It would avoid the complexity and overhead of publishing the data sets on the Snowflake Marketplace, and the potential loss of control over the data ownership and pricing.
It would avoid the need to create a database user for the partner and grant them access to the required data sets, which would require the partner to have a Snowflake account and consume the provider's resources.
Reader Accounts
Secure Views
Which organization-related tasks can be performed by the ORGADMIN role? (Choose three.)
According to the SnowPro Advanced: Architect documents and learning resources, the organization-related tasks that can be performed by the ORGADMIN role are:
Creating an account in the organization.A user with the ORGADMIN role can use the CREATE ACCOUNT command to create a new account that belongs to the same organization as the current account1.
Viewing a list of organization accounts.A user with the ORGADMIN role can use the SHOW ORGANIZATION ACCOUNTS command to view the names and properties of all accounts in the organization2.Alternatively, the user can use the Admin Accounts page in the web interface to view the organization name and account names3.
Enabling the replication of a database. A user with the ORGADMIN role can use the SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER function to enable database replication for an account in the organization.This allows the user to replicate databases across accounts in different regions and cloud platforms for data availability and durability4.
The other options are incorrect because they are not organization-related tasks that can be performed by the ORGADMIN role. Option A is incorrect because changing the name of the organization is not a task that can be performed by the ORGADMIN role.To change the name of an organization, the user must contact Snowflake Support3. Option D is incorrect because changing the name of an account is not a task that can be performed by the ORGADMIN role.To change the name of an account, the user must contact Snowflake Support5. Option E is incorrect because deleting an account is not a task that can be performed by the ORGADMIN role. To delete an account, the user must contact Snowflake Support.Reference:CREATE ACCOUNT | Snowflake Documentation,SHOW ORGANIZATION ACCOUNTS | Snowflake Documentation,Getting Started with Organizations | Snowflake Documentation,SYSTEM$GLOBAL_ACCOUNT_SET_PARAMETER | Snowflake Documentation,ALTER ACCOUNT | Snowflake Documentation, [DROP ACCOUNT | Snowflake Documentation]
Which Snowflake data modeling approach is designed for BI queries?
In the context of business intelligence (BI) queries, which are typically focused on data analysis and reporting, the star schema is the most suitable data modeling approach.
Option B: Star Schema - The star schema is a type of relational database schema that is widely used for developing data warehouses and data marts for BI purposes. It consists of a central fact table surrounded by dimension tables. The fact table contains the core data metrics, and the dimension tables contain descriptive attributes related to the fact data. The simplicity of the star schema allows for efficient querying and aggregation, which are common operations in BI reporting.
What are characteristics of the use of transactions in Snowflake? (Select TWO).
A. Snowflake's transactions can indeed include DDL (Data Definition Language), DML (Data Manipulation Language), and query statements. When executed within a transaction block, they all contribute to the atomicity of the transaction---either all of them commit together or none at all. C. Snowflake supports explicit transaction control through the use of the BEGIN TRANSACTION (or simply BEGIN) and COMMIT statements. Alternatively, the BEGIN WORK and COMMIT WORK syntax is also supported, which is a standard SQL syntax for initiating and ending transactions, respectively. Note: The END TRANSACTION statement is not used in Snowflake to end a transaction; the correct statement is COMMIT or COMMIT WORK.
A retail company has over 3000 stores all using the same Point of Sale (POS) system. The company wants to deliver near real-time sales results to category managers. The stores operate in a variety of time zones and exhibit a dynamic range of transactions each minute, with some stores having higher sales volumes than others.
Sales results are provided in a uniform fashion using data engineered fields that will be calculated in a complex data pipeline. Calculations include exceptions, aggregations, and scoring using external functions interfaced to scoring algorithms. The source data for aggregations has over 100M rows.
Every minute, the POS sends all sales transactions files to a cloud storage location with a naming convention that includes store numbers and timestamps to identify the set of transactions contained in the files. The files are typically less than 10MB in size.
How can the near real-time results be provided to the category managers? (Select TWO).
To provide near real-time sales results to category managers, the Architect can use the following steps:
Create an external stage that references the cloud storage location where the POS sends the sales transactions files.The external stage should use the file format and encryption settings that match the source files2
Create a Snowpipe that loads the files from the external stage into a target table in Snowflake. The Snowpipe should be configured with AUTO_INGEST = true, which means that it will automatically detect and ingest new files as they arrive in the external stage.The Snowpipe should also use a copy option to purge the files from the external stage after loading, to avoid duplicate ingestion3
Create a stream on the target table that captures the INSERTS made by the Snowpipe. The stream should include the metadata columns that provide information about the file name, path, size, and last modified time.The stream should also have a retention period that matches the real-time analytics needs4
Create a task that runs a query on the stream to process the near real-time data. The query should use the stream metadata to extract the store number and timestamps from the file name and path, and perform the calculations for exceptions, aggregations, and scoring using external functions. The query should also output the results to another table or view that can be accessed by the category managers. The task should be scheduled to run at a frequency that matches the real-time analytics needs, such as every minute or every 5 minutes.
The other options are not optimal or feasible for providing near real-time results:
All files should be concatenated before ingestion into Snowflake to avoid micro-ingestion. This option is not recommended because it would introduce additional latency and complexity in the data pipeline. Concatenating files would require an external process or service that monitors the cloud storage location and performs the file merging operation. This would delay the ingestion of new files into Snowflake and increase the risk of data loss or corruption. Moreover, concatenating files would not avoid micro-ingestion, as Snowpipe would still ingest each concatenated file as a separate load.
An external scheduler should examine the contents of the cloud storage location and issue SnowSQL commands to process the data at a frequency that matches the real-time analytics needs. This option is not necessary because Snowpipe can automatically ingest new files from the external stage without requiring an external trigger or scheduler. Using an external scheduler would add more overhead and dependency to the data pipeline, and it would not guarantee near real-time ingestion, as it would depend on the polling interval and the availability of the external scheduler.
The copy into command with a task scheduled to run every second should be used to achieve the near-real time requirement. This option is not feasible because tasks cannot be scheduled to run every second in Snowflake. The minimum interval for tasks is one minute, and even that is not guaranteed, as tasks are subject to scheduling delays and concurrency limits. Moreover, using the copy into command with a task would not leverage the benefits of Snowpipe, such as automatic file detection, load balancing, and micro-partition optimization.Reference:
1: SnowPro Advanced: Architect | Study Guide
2: Snowflake Documentation | Creating Stages
3: Snowflake Documentation | Loading Data Using Snowpipe
4: Snowflake Documentation | Using Streams and Tasks for ELT
: Snowflake Documentation | Creating Tasks
: Snowflake Documentation | Best Practices for Loading Data
: Snowflake Documentation | Using the Snowpipe REST API
: Snowflake Documentation | Scheduling Tasks
:SnowPro Advanced: Architect | Study Guide
:Creating Stages
:Loading Data Using Snowpipe
:Using Streams and Tasks for ELT
: [Creating Tasks]
: [Best Practices for Loading Data]
: [Using the Snowpipe REST API]
: [Scheduling Tasks]
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