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Snowflake ARA-C01  Exam Questions & Answers
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Snowflake ARA-C01 Exam Questions & Answers


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  • Vendor: Snowflake

    Exam Code: ARA-C01

    Exam Name: SnowPro Advanced: Architect Certification (ARA-C01)

    Certification Provider: Snowflake

    Total Questions: 65 Q&A ( View Details)

    Updated on: Jun 10, 2025

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ARA-C01 Online Practice Questions and Answers

Questions 1

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. Define row access policies.

B. Use theBusiness-Criticaledition of Snowflake.

C. Create a secure view.

D. Use the Enterprise edition of Snowflake.

E. Use Dynamic Data Masking.

F. Create a materialized view.

Show Answer

Correct Answer: BCE

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.

Questions 2

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 raw database for landing and persisting raw data entering the data pipelines.

C. Create a set of profile-specific databases that aligns data with usage patterns.

D. Create a single star schema in a single database to support all consumers' requirements.

E. Create a Data Vault as the sole data pipeline endpoint and have all consumers directly access the Vault.

Show Answer

Correct Answer: BC

Explanation: These two approaches are recommended by Snowflake for data modeling in a data lake scenario. Creating a raw database allows the data engineering team to ingest data from various sources without any transformation or cleansing, preserving the original data quality and format. This enables the data science team to access the raw data for ML model development. Creating a set of profile-specific databases allows the data engineering team to apply different transformations and optimizations for different use cases and data consumer requirements. For example, the finance and vendor management team can access a dimensional database that supports reporting and visualization, while the sales team can access a secure database that supports data monetization. References: Snowflake Data Lake Architecture | Snowflake Documentation Snowflake Data Lake Best Practices | Snowflake Documentation

Questions 3

A company's daily Snowflake workload consists of a huge number of concurrent queries triggered between 9pm and 11pm. At the individual level, these queries are smaller statements that get completed within a short time period.

What configuration can the company's Architect implement to enhance the performance of this workload? (Choose two.)

A. Enable a multi-clustered virtual warehouse in maximized mode during the workload duration.

B. Set the MAX_CONCURRENCY_LEVEL to a higher value than its default value of 8 at the virtual warehouse level.

C. Increase the size of the virtual warehouse to size X-Large.

D. Reduce the amount of data that is being processed through this workload.

E. Set the connection timeout to a higher value than its default.

Show Answer More Questions

Correct Answer: AB

Explanation: These two configuration options can enhance the performance of the workload that consists of a huge number of concurrent queries that are smaller and faster. Enabling a multi-clustered virtual warehouse in maximized mode allows the warehouse to scale out automatically by adding more clusters as soon as the current cluster is fully loaded, regardless of the number of queries in the queue. This can improve the concurrency and throughput of the workload by minimizing or preventing queuing. The maximized mode is suitable for workloads that require high performance and low latency, and are less sensitive to credit consumption1. Setting the MAX_CONCURRENCY_LEVEL to a higher value than its default value of 8 at the virtual warehouse level allows the warehouse to run more queries concurrently on each cluster. This can improve the utilization and efficiency of the warehouse resources, especially for smaller and faster queries that do not require a lot of processing power. The MAX_CONCURRENCY_LEVEL parameter can be set when creating or modifying a warehouse, and it can be changed at any time2. References: Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses Snowflake Documentation: MAX_CONCURRENCY_LEVEL

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