Snowflake ARA-R01 Exam Dumps

Get All SnowPro Advanced: Architect Recertification Exam Questions with Validated Answers

ARA-R01 Pack
Vendor: Snowflake
Exam Code: ARA-R01
Exam Name: SnowPro Advanced: Architect Recertification
Exam Questions: 162
Last Updated: October 4, 2026
Related Certifications: SnowPro Certification
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Free Snowflake ARA-R01 Exam Actual Questions

Question No. 1

A Data Engineer is designing a near real-time ingestion pipeline for a retail company to ingest event logs into Snowflake to derive insights. A Snowflake Architect is asked to define security best practices to configure access control privileges for the data load for auto-ingest to Snowpipe.

What are the MINIMUM object privileges required for the Snowpipe user to execute Snowpipe?

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Correct Answer: B

According to the SnowPro Advanced: Architect documents and learning resources, the minimum object privileges required for the Snowpipe user to execute Snowpipe are:

OWNERSHIP on the named pipe.This privilege allows the Snowpipe user to create, modify, and drop the pipe object that defines the COPY statement for loading data from the stage to the table1.

USAGE and READ on the named stage.These privileges allow the Snowpipe user to access and read the data files from the stage that are loaded by Snowpipe2.

USAGE on the target database and schema.These privileges allow the Snowpipe user to access the database and schema that contain the target table3.

INSERT and SELECT on the target table.These privileges allow the Snowpipe user to insert data into the table and select data from the table4.

The other options are incorrect because they do not specify the minimum object privileges required for the Snowpipe user to execute Snowpipe. Option A is incorrect because it does not include the READ privilege on the named stage, which is required for the Snowpipe user to read the data files from the stage. Option C is incorrect because it does not include the OWNERSHIP privilege on the named pipe, which is required for the Snowpipe user to create, modify, and drop the pipe object. Option D is incorrect because it does not include the OWNERSHIP privilege on the named pipe or the READ privilege on the named stage, which are both required for the Snowpipe user to execute Snowpipe.Reference:CREATE PIPE | Snowflake Documentation,CREATE STAGE | Snowflake Documentation,CREATE DATABASE | Snowflake Documentation,CREATE TABLE | Snowflake Documentation


Question No. 2

An Architect needs to design a data unloading strategy for Snowflake, that will be used with the COPY INTO command.

Which configuration is valid?

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Correct Answer: C

For the configuration of data unloading in Snowflake, the valid option among the provided choices is 'C.' This is because Snowflake supports unloading data into Google Cloud Storage using the COPY INTO <location> command with specific configurations. The configurations listed in option C, such as Parquet file format with UTF-8 encoding and gzip compression, are all supported by Snowflake. Notably, Parquet is a columnar storage file format, which is optimal for high-performance data processing tasks in Snowflake. The UTF-8 file encoding and gzip compression are both standard and widely used settings that are compatible with Snowflake's capabilities for data unloading to cloud storage platforms. Reference:

Snowflake Documentation on COPY INTO command

Snowflake Documentation on Supported File Formats

Snowflake Documentation on Compression and Encoding Options


Question No. 3

A healthcare company is deploying a Snowflake account that may include Personal Health Information (PHI). The company must ensure compliance with all relevant privacy standards.

Which best practice recommendations will meet data protection and compliance requirements? (Choose three.)

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Correct Answer: A, B, D

A healthcare company that handles PHI data must ensure compliance with relevant privacy standards, such as HIPAA, HITRUST, and GDPR.Snowflake provides several features and best practices to help customers meet their data protection and compliance requirements1.

One best practice recommendation is to use, at minimum, the Business Critical edition of Snowflake.This edition provides the highest level of data protection and security, including end-to-end encryption with customer-managed keys, enhanced object-level security, and HIPAA and HITRUST compliance2. Therefore, option A is correct.

Another best practice recommendation is to create Dynamic Data Masking policies and apply them to columns that contain PHI. Dynamic Data Masking is a feature that allows masking or redacting sensitive data based on the current user's role.This way, only authorized users can view the unmasked data, while others will see masked values, such as NULL, asterisks, or random characters3. Therefore, option B is correct.

A third best practice recommendation is to use the External Tokenization feature to obfuscate sensitive data. External Tokenization is a feature that allows replacing sensitive data with tokens that are generated and stored by an external service, such as Protegrity.This way, the original data is never stored or processed by Snowflake, and only authorized users can access the tokenized data through the external service4. Therefore, option D is correct.

Option C is incorrect, because the Internal Tokenization feature is not available in Snowflake.Snowflake does not provide any native tokenization functionality, but only supports integration with external tokenization services4.

Option E is incorrect, because rewriting SQL queries to eliminate projections of PHI data based on current_role() is not a best practice. This approach is error-prone, inefficient, and hard to maintain.A better alternative is to use Dynamic Data Masking policies, which can automatically mask data based on the user's role without modifying the queries3.

Option F is incorrect, because avoiding sharing data with partner organizations is not a best practice. Snowflake enables secure and governed data sharing with internal and external consumers, such as business units, customers, or partners. Data sharing does not involve copying or moving data, but only granting access privileges to the shared objects.Data sharing can also leverage Dynamic Data Masking and External Tokenization features to protect sensitive data5.


Question No. 4

What does a Snowflake Architect need to consider when implementing a Snowflake Connector for Kafka?

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Correct Answer: D

The Snowflake Connector for Kafka is a Kafka Connect sink connector that reads data from one or more Apache Kafka topics and loads the data into a Snowflake table. The connector supports different authentication methods to connect to Snowflake, such as key pair authentication, OAUTH, and basic authentication (for example, username and password).The connector also supports different encryption methods, such as HTTPS and SSL1.The connector does not require that every Kafka message is in JSON or Avro format, as it can handle other formats such as CSV, XML, and Parquet2. The default retention time for Kafka topics is not relevant for the connector, as it only consumes the messages that are available in the topics and does not store them in Kafka.The connector will create one table and one pipe to ingest data for each topic by default, but this behavior can be customized by using the snowflake.topic2table.map configuration property3.If the connector cannot create the table or the pipe, it will log an error and retry the operation until it succeeds or the connector is stopped4.Reference:

Installing and Configuring the Kafka Connector

Overview of the Kafka Connector

Managing the Kafka Connector

Troubleshooting the Kafka Connector


Question No. 5

What step will im the performance of queries executed against an external table?

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Correct Answer: A

Partitioning an external table is a technique that improves the performance of queries executed against the table by reducing the amount of data scanned. Partitioning an external table involves creating one or more partition columns that define how the table is logically divided into subsets of data based on the values in those columns. The partition columns can be derived from the file metadata (such as file name, path, size, or modification time) or from the file content (such as a column value or a JSON attribute).Partitioning an external table allows the query optimizer to prune the files that do not match the query predicates, thus avoiding unnecessary data scanning and processing2

The other options are not effective steps for improving the performance of queries executed against an external table:

Shorten the names of the source files. This option does not have any impact on the query performance, as the file names are not used for query processing.The file names are only used for creating the external table and displaying the query results3

Convert the source files' character encoding to UTF-8. This option does not affect the query performance, as Snowflake supports various character encodings for external table files, such as UTF-8, UTF-16, UTF-32, ISO-8859-1, and Windows-1252.Snowflake automatically detects the character encoding of the files and converts them to UTF-8 internally for query processing4

Use an internal stage instead of an external stage to store the source files. This option is not applicable, as external tables can only reference files stored in external stages, such as Amazon S3, Google Cloud Storage, or Azure Blob Storage.Internal stages are used for loading data into internal tables, not external tables5Reference:

1: SnowPro Advanced: Architect | Study Guide

2: Snowflake Documentation | Partitioning External Tables

3: Snowflake Documentation | Creating External Tables

4: Snowflake Documentation | Supported File Formats and Compression for Staged Data Files

5: Snowflake Documentation | Overview of Stages

:SnowPro Advanced: Architect | Study Guide

:Partitioning External Tables

:Creating External Tables

:Supported File Formats and Compression for Staged Data Files

:Overview of Stages


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