ARA-C01 Snowflake SnowPro Advanced: Architect Certification Exam Free Practice Exam Questions (2026 Updated)

The requirements state that the data analysts need to be able to create and modify database objects and load data, but should not be able to manage access for users outside of their role.

Option C:By making each schema within the database a managed access schema and having them owned by SYSADMIN, the ability to grant privileges on the schema's objects is strictly controlled. Managed access schemas limit the granting of privileges to the role specified as the owner of the schema, in this case, SYSADMIN. The ANALYST_ROLE can be granted the privileges necessary to create and modify objects within these schemas, satisfying the requirement for the analysts to perform their tasks without being able to extend access beyond their role.

This is the valid object hierarchy when building a Snowflake environment, according to the Snowflake documentation and the web search results. Snowflake is a cloud data platform that supports various types of objects, such as databases, schemas, tables, views, stages, warehouses, and more. These objects are organized in a hierarchical structure, as follows:

Organization: An organization is the top-level entity that represents a group of Snowflake accounts that are related by business needs or ownership. An organization can have one or more accounts, and can enable features such as cross-account data sharing, billing and usage reporting, and single sign-on across accounts12.

Account: An account is the primary entity that represents a Snowflake customer. An account can have one or more databases, schemas, stages, warehouses, and other objects. An account can also have one or more users, roles, and security integrations. An account is associated with a specific cloud platform, region, and Snowflake edition34.

Database: A database is a logical grouping of schemas. A database can have one or more schemas, and can store structured, semi-structured, or unstructured data. A database can also have properties such as retention time, encryption, and ownership56.

Schema: A schema is a logical grouping of tables, views, stages, and other objects. A schema can have one or more objects, and can define the namespace and access control for the objects. A schema can also have properties such as ownership and default warehouse .

Stage: A stage is a named location that references the files in external or internal storage. A stage can be used to load data into Snowflake tables using the COPY INTO command, or to unload data from Snowflake tables using the COPY INTO LOCATION command. A stage can be created at the account, database, or schema level, and can have properties such as file format, encryption, and credentials .

The other options listed are not valid object hierarchies, because they either omit or misplace some objects in the structure. For example, option A omits the organization level and places the warehouse under the schema level, which is incorrect. Option C omits the organization, account, and stage levels, and places the table under the schema level, which is incorrect. Option D omits the database level and places the stage and table under the account level, which is incorrect.

Snowflake Documentation: Organizations

Snowflake Blog: Introducing Organizations in Snowflake

Snowflake Documentation: Accounts

Snowflake Blog: Understanding Snowflake Account Structures

Snowflake Documentation: Databases

Snowflake Blog: How to Create a Database in Snowflake

[Snowflake Documentation: Schemas]

[Snowflake Blog: How to Create a Schema in Snowflake]

[Snowflake Documentation: Stages]

[Snowflake Blog: How to Use Stages in Snowflake]

The most cost-effective and administratively efficient solution is to use a combination of native and external tables. Native tables for reporting data ensure performance and governance, while external tables allow for flexibility with frequently changing experimental data. Creating roles with specific grants to datasets aligns with the principle of least privilege, centralizing access control and simplifying user management12.

References

•Snowflake Documentation on Optimizing Cost1.

•Snowflake Documentation on Controlling Cost2.

Warehouse credits are used to pay for the processing time used by each virtual warehouse in Snowflake. A virtual warehouse is a cluster of compute resources that enables executing queries, loading data, and performing other DML operations. Warehouse credits are charged based on the number of virtual warehouses you use, how long they run, and their size1.

Among the commands listed in the question, the following ones will use warehouse credits:

SELECT MAX(FLAKE_ID) FROM SNOWFLAKE: This command will use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the maximum value of the FLAKE_ID column2. Therefore, option B is correct.

SELECT COUNT(*) FROM SNOWFLAKE: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows in the table3. Therefore, option C is correct.

SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID: This command will also use warehouse credits because it is a query that requires a virtual warehouse to execute. The query will scan the SNOWFLAKE table and return the number of rows for each distinct value of the FLAKE_ID column4. Therefore, option D is correct.

The command that will not use warehouse credits is:

SHOW TABLES LIKE ‘SNOWFL%’: This command will not use warehouse credits because it is a metadata operation that does not require a virtual warehouse to execute. The command will return the names of the tables that match the pattern ‘SNOWFL%’ in the current database and schema5. Therefore, option A is incorrect.

 Understanding Compute Cost : MAX Function : COUNT Function : GROUP BY Clause : SHOW TABLES

Snowflake external tables support partitioning using the PARTITION BY clause, which allows architects to define logical partitions based on expressions derived from file paths or metadata. This approach is flexible and scalable, making it well-suited for data lakes that grow over time and may require changes to partition logic (Answer A).

Using PARTITION BY enables Snowflake to automatically manage partitions as new data arrives, without requiring manual intervention or table recreation. This aligns with Snowflake best practices for external table design and minimizes operational overhead.

The other options are invalid or unsupported in Snowflake. There is no partition_type = USER_SPECIFIED option, no METADATA$EXTERNAL_TABLE_PARTITION = MANUAL setting, and no ADD_PARTITION_COLUMN command for external tables. For the SnowPro Architect exam, understanding the correct and supported syntax for partitioned external tables is essential for designing maintainable data lake architectures.

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QUESTION NO: 62 [Snowflake Ecosystem and Integrations]

A group of marketing and advertising companies want to share data with one another for joint analysis while maintaining strict privacy controls.

How should an Architect design the data sharing solution?

A. Use Data Clean Rooms for privacy-preserving collaboration.

B. Use Secure Data Sharing with standard shares.

C. Use Snowflake Marketplace listings.

D. Use a Data Exchange as a shared data hub.

Answer: A

Snowflake Data Clean Rooms are specifically designed for privacy-preserving collaboration between organizations. They allow multiple parties to perform joint analytics on combined datasets without exposing raw underlying data to one another (Answer A). This makes them ideal for marketing and advertising use cases where sensitive customer data must remain private while still enabling insights such as audience overlap and campaign effectiveness.

Secure Data Sharing and Data Exchanges allow data access but do not inherently prevent participants from seeing raw data. Snowflake Marketplace is designed for broad data distribution rather than tightly controlled, privacy-centric collaboration.

For SnowPro Architect candidates, this question emphasizes selecting the right Snowflake-native collaboration mechanism based on privacy and governance requirements.

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QUESTION NO: 63 [Snowflake Data Engineering]

An Architect wants to build an automated ETL pipeline that reads data from an external stage using an external table, performs transformations on changed data, joins with dimension tables, and loads results into a target table.

What should the Architect use?

A. Streams and tasks

B. Dynamic tables

C. Materialized views

D. Snowpipe with auto-ingest

Answer: A

Streams and tasks provide Snowflake’s native framework for building automated, incremental ETL pipelines. A stream can track changes in the external table (or staging table), while tasks orchestrate the execution of transformation logic and loading into target tables (Answer A).

This approach supports complex transformations, joins with dimension tables, and controlled scheduling or event-driven execution. Materialized views cannot perform joins with arbitrary tables and are not suitable for complex ETL. Dynamic tables simplify transformations but are not designed to consume change data directly from external tables. Snowpipe focuses on ingestion only and does not support downstream transformations.

SnowPro Architect exams frequently test understanding of when to use streams and tasks versus newer abstractions like dynamic tables.

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QUESTION NO: 64 [Security and Access Management]

A data share exists between a provider and a consumer account. Five tables are already shared, and the consumer role has been granted IMPORTED PRIVILEGES.

What happens if a new table is added to the provider schema?

A. The consumer automatically sees the new table.

B. The consumer sees the table after granting IMPORTED PRIVILEGES again on the consumer side.

C. The consumer sees the table only after SELECT is granted on the new table to the share on the provider side.

D. The consumer sees the table after USAGE is granted on the database and schema and SELECT is granted on the table to the consumer database.

Answer: C

In Snowflake Secure Data Sharing, adding a new table to an existing schema does not automatically make it visible to consumers. The provider must explicitly grant SELECT on the new table to the share (Answer C). This ensures intentional and controlled data exposure.

Once the grant is applied on the provider side, consumer roles that already have IMPORTED PRIVILEGES will automatically see the new table without requiring additional grants on the consumer account. This separation of responsibilities is a core Snowflake governance principle.

This question reinforces SnowPro Architect knowledge of provider-versus-consumer responsibilities in Secure Data Sharing.

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QUESTION NO: 65 [Snowflake Data Engineering]

Which technique efficiently ingests and consumes semi-structured data for Snowflake data lake workloads?

A. IDEF1X

B. Schema-on-write

C. Schema-on-read

D. Information schema

Answer: C

Schema-on-read is a fundamental pattern for Snowflake data lake workloads involving semi-structured data such as JSON, Avro, or Parquet (Answer C). Data is ingested in its raw form and interpreted at query time using Snowflake’s VARIANT type and functions like FLATTEN.

This approach provides flexibility as data structures evolve, reduces ingestion complexity, and avoids the need to predefine rigid schemas. Schema-on-write is more appropriate for structured data warehouses but is less efficient for rapidly changing semi-structured data. IDEF1X is a data modeling notation, not an ingestion technique. Information schema is a metadata repository.

For SnowPro Architect candidates, understanding schema-on-read is critical when designing scalable, flexible data lake architectures in Snowflake.

Snowflake supports cloning of various objects, such as databases, schemas, tables, stages, file formats, sequences, streams, tasks, and roles. Cloning creates a copy of an existing object in the system without copying the data or metadata. Cloning is also known as zero-copy cloning1.

Among the objects listed in the question, the following ones can be cloned in Snowflake:

Permanent table: A permanent table is a type of table that has a Fail-safe period and a Time Travel retention period of up to 90 days. A permanent table can be cloned using the CREATE TABLE … CLONE command2. Therefore, option A is correct.

Transient table: A transient table is a type of table that does not have a Fail-safe period and can have a Time Travel retention period of either 0 or 1 day. A transient table can also be cloned using the CREATE TABLE … CLONE command2. Therefore, option B is correct.

External table: An external table is a type of table that references data files stored in an external location, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. An external table can be cloned using the CREATE EXTERNAL TABLE … CLONE command3. Therefore, option D is correct.

The following objects listed in the question cannot be cloned in Snowflake:

Temporary table: A temporary table is a type of table that is automatically dropped when the session ends or the current user logs out. Temporary tables do not support cloning4. Therefore, option C is incorrect.

Internal stage: An internal stage is a type of stage that is managed by Snowflake and stores files in Snowflake’s internal cloud storage. Internal stages do not support cloning5. Therefore, option E is incorrect.

 Cloning Considerations : CREATE TABLE … CLONE : CREATE EXTERNAL TABLE … CLONE : Temporary Tables : Internal Stages

According to the SnowPro Advanced: Architect documents and learning resources, the best way to enable optimal clustering to enhance performance for different access paths on a given table is to create multiple materialized views with different cluster keys. A materialized view is a pre-computed result set that is derived from a query on one or more base tables. A materialized view can be clustered by specifying a clustering key, which is a subset of columns or expressions that determines how the data in the materialized view is co-located in micro-partitions. By creating multiple materialized views with different cluster keys, an Architect can optimize the performance of queries that use different access paths on the same base table. For example, if a base table has columns A, B, C, and D, and there are queries that filter on A and B, or on C and D, or on A and C, the Architect can create three materialized views, each with a different cluster key: (A, B), (C, D), and (A, C). This way, each query can leverage the optimal clustering of the corresponding materialized view and achieve faster scan efficiency and better compression.

Snowflake Documentation: Materialized Views

Snowflake Learning: Materialized Views

https://www.snowflake.com/blog/using-materialized-views-to-solve-multi-clustering-performance-problems/

This question evaluates deep understanding of Snowflake RBAC, managed access schemas, and privilege inheritance, all of which are core SnowPro Architect topics. The schema DEV_TEST_DB.SCHTEST is created WITH MANAGED ACCESS, meaning that only the schema owner (or a higher role in the hierarchy) can grant object privileges such as SELECT on tables within the schema.

The table CURRENCY is created by the role DEV_PROJ_OWN, making it the table owner. As the owner, DEV_PROJ_OWN implicitly has full privileges on the table, including SELECT. Therefore, option B succeeds because the role selecting the data owns the table.

In option A, DEV_PROJ_OWN explicitly grants SELECT on the table to ANALYST_PROJ. Since this grant is performed by the schema/table owner and the role ANALYST_PROJ already has USAGE on both the database and schema, the subsequent SELECT succeeds. This makes A valid.

Option C fails because SYSADMIN does not inherit privileges from DEV_SYSADMIN or DEV_PROJ_OWN; Snowflake role inheritance is directional and not lateral. Option D fails for the same reason—MAPPING_ROLE has no privileges on the database or schema. Option E fails because ACCOUNTADMIN does not automatically bypass RBAC; without explicit USAGE and SELECT, access is denied.

Snowflake’s Time Travel feature allows users to query historical data within a defined retention period. In the given scenario, since the secondary database is refreshed every hour, Time Travel can be used to query each hourly version of the table as long as it falls within the retention window. This does not include individual Snowpipe loads within each hour unless they coincide with the hourly refresh.

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.

In Snowflake, the characteristics of result set caches include persistence of data results for 24 hours (B), each use of persisted results resets the 24-hour retention period (C), and result set caches are not shared between different warehouses (F). The result set cache is specifically designed to avoid repeated execution of the same query within this timeframe, reducing computational overhead and speeding up query responses. These caches do not contribute to storage costs, and their retention period cannot be extended beyond the default duration nor up to 31 days, as might be misconstrued.

Enabling the search optimization service on the table can improve the performance of queries that have selective filtering criteria, which seems to be the case here. This service optimizes the execution of queries by creating a persistent data structure called a search access path, which allows some micro-partitions to be skipped during the scanning process. This can significantly speed up query performance without increasing compute costs1.

References

•Snowflake Documentation on Search Optimization Service1.

The correct way to securely share data with a vendor using a Snowflake account on a different cloud platform and region is to create a share, add objects to the share, and add a consumer account to the share for the vendor to access. This way, the company can control what data is shared, who can access it, and how long the share is valid. The vendor can then query the shared data without copying or moving it to their own account. The other options are either incorrect or inefficient, as they involve creating unnecessary reader accounts, users, roles, or database replication.

https://learn.snowflake.com/en/certifications/snowpro-advanced-architect/

This question tests understanding of Snowflake Role-Based Access Control (RBAC) and privilege inheritance, which is a core SnowPro Architect exam topic. In Snowflake, privileges are not granted directly to users; instead, they are granted to roles, which are then assigned to users. Effective access depends on the combination of USAGE and SELECT privileges across databases, schemas, and tables.

In the provided design, the role DB_IT_RO_ROLE has SELECT privileges on all tables in the database, along with database usage. This role is granted to IT_ANALYST_ROLE, which is assigned to User1. As a result, User1 can read tables from both IT_SCHEMA and FINANCE_SCHEMA, assuming schema usage is satisfied, which is implied in the diagram.

User2, on the other hand, is granted the FIN_ANALYST_ROLE, which inherits privileges from DB_FIN_ROLE. That role only has USAGE and SELECT privileges on the FINANCE_SCHEMA. There are no grants that allow DB_FIN_ROLE (or FIN_ANALYST_ROLE) to access objects in IT_SCHEMA.

According to the Snowflake documentation, the data loading performance can be improved by following some best practices and guidelines for preparing and staging the data files. One of the recommendations is to aim for data files that are roughly 100-250 MB (or larger) in size compressed, as this will optimize the number of parallel operations for a load. Smaller files should be aggregated and larger files should be split to achieve this size range. Another recommendation is to use a multi-cluster warehouse for loading, as this will allow for scaling up or out the compute resources depending on the load demand. A single-cluster warehouse may not be able to handle the load concurrency and throughput efficiently. Therefore, by creating a multi-cluster warehouse and merging smaller files to create bigger files, the data loading performance can be improved. References:

Data Loading Considerations

Preparing Your Data Files

Planning a Data Load

According to the Snowflake documentation, tasks are objects that enable scheduling and execution of SQL statements or JavaScript user-defined functions (UDFs) in Snowflake. Tasks can be used to automate data loading, transformation, and maintenance operations. Snowflake scripting is a feature that allows writing procedural logic using SQL statements and JavaScript UDFs. Snowflake scripting can be used to create complex workflows and orchestrate tasks. Therefore, the best option to automate the daily import of two files from an external stage into Snowflake, join and aggregate the data, and produce a final result set is to create a task using Snowflake scripting that will import the files using the COPY INTO command, and then call a UDF to perform the join and aggregation logic. The UDF can return a table or a variant value as the final result set. References:

Tasks

Snowflake Scripting

User-Defined Functions

The image shows a SQL query that can be used to identify which queries are spilled to remote storage and suggests changing the warehouse parameters to address this issue. Spilling to remote storage occurs when the memory allocated to a warehouse is insufficient to process a query, and Snowflake uses disk or cloud storage as a temporary cache. This can significantly slow down the query performance and increase the cost. To troubleshoot this issue, a Snowflake Architect can run the query shown in the image to find out which queries are spilling, how much data they are spilling, and which warehouses they are using. Then, the architect can adjust the warehouse size, type, or scaling policy to provide enough memory for the queries and avoid spilling12. References:

Recognizing Disk Spilling

Managing the Kafka Connector

 These two features are relevant for modeling using Data Vault on Snowflake. Data Vault is a data modeling approach that organizes data into hubs, links, and satellites. Data Vault is designed to enable high scalability, flexibility, and performance for data integration and analytics. Snowflake is a cloud data platform that supports various data modeling techniques, including Data Vault. Snowflake provides some features that can enhance the Data Vault modeling, such as:

Snowflake’s support of multi-table inserts into the data model’s Data Vault tables. Multi-table inserts (MTI) are a feature that allows inserting data from a single query into multiple tables in a single DML statement. MTI can improve the performance and efficiency of loading data into Data Vault tables, especially for real-time or near-real-time data integration. MTI can also reduce the complexity and maintenance of the loading code, as well as the data duplication and latency12.

Scaling up the virtual warehouses will support parallel processing of new source loads. Virtual warehouses are a feature that allows provisioning compute resources on demand for data processing. Virtual warehouses can be scaled up or down by changing the size of the warehouse, which determines the number of servers in the warehouse. Scaling up the virtual warehouses can improve the performance and concurrency of processing new source loads into Data Vault tables, especially for large or complex data sets. Scaling up the virtual warehouses can also leverage the parallelism and distribution of Snowflake’s architecture, which can optimize the data loading and querying34.

Snowflake Documentation: Multi-table Inserts

Snowflake Blog: Tips for Optimizing the Data Vault Architecture on Snowflake

Snowflake Documentation: Virtual Warehouses

Snowflake Blog: Building a Real-Time Data Vault in Snowflake

This scenario tests understanding of Snowflake Time Travel and operational efficiency, both key topics in the SnowPro Architect exam. When a table is replaced using CREATE OR REPLACE TABLE, Snowflake treats the original table version as dropped but still recoverable within the Time Travel retention period. The goal is to recover the original data with the least effort and risk.

Option A is the most efficient solution. Using Time Travel with the BEFORE (STATEMENT => …) clause allows the Architect to query the exact state of the table immediately before the replacement occurred. This approach precisely targets the moment before the destructive operation, avoids guessing timestamps, and minimizes operational steps. It is also highly accurate, which is critical in production recovery scenarios.

Option B relies on a timestamp, which introduces risk if the timestamp is incorrect or ambiguous. Option C is invalid because UNDROP TABLE only works when a table has been explicitly dropped, not replaced. Option D introduces unnecessary steps and operational overhead, increasing the risk of mistakes.