Snowflake · SF-ARC · Advanced

SnowPro Advanced: Architect (ARA-C01) — Practice Questions and Mock Exam

Name: Pass-IT
Author: Pass-IT

The SnowPro Advanced: Architect Certification (ARA-C01) validates the ability to design end-to-end data architectures on Snowflake, including data flow from source to consumption, compliance-aware security design, performance optimization using Snowflake and third-party tools, and deploying shared datasets via Snowflake Marketplace and Data Exchange.

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Official SF-ARC exam: 750 out of 1000 points to pass.

Checked against Snowflake · September 2026 · Current exam version

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Accounts and Security1 / 5

What is the purpose of tag-based masking in Snowflake?

AlexFull explanation from Alex

Tag-based masking automatically applies masking policies to columns carrying a specific tag. Per Snowflake docs (docs.snowflake.com/en/user-guide/tag-based-masking-policies), you associate a masking policy with a tag via ALTER TAG; when that tag is assigned to a column whose data type matches the policy signature, masking is enforced automatically. This enables centralized, scalable governance—one tag assignment protects all matching columns across tables. The option “To mask data based on its storage format” is wrong: masking controls access to values, not storage format. The option “To mask column names in query results” is wrong: column names are never masked; column values are. The option “To encrypt tag metadata in the catalog” is wrong: tag metadata is not encrypted by masking policies; encryption is a separate concern.

Sourcedocs.snowflake.com

Snowflake Architecture2 / 5

What is the Unistore workload architecture in Snowflake designed for?

AlexFull explanation from Alex

Unistore is Snowflake's workload architecture for combining transactional (OLTP) and analytical (OLAP) workloads using hybrid tables. Per Snowflake docs (docs.snowflake.com/en/user-guide/tables-hybrid), hybrid tables use row-based storage with row-level locking for fast point lookups and DML while supporting analytical queries through the same engine. The option “Unifying streaming and batch processing” is wrong: streaming/batch unification is handled by Snowpipe and tasks, not Unistore. The option “Providing a universal data format across cloud providers” is wrong: Unistore addresses workload convergence, not data format portability across clouds. The option “Unifying structured and unstructured data storage” is wrong: while Snowflake supports semi-structured data natively, Unistore specifically targets OLTP+OLAP convergence.

Sourcedocs.snowflake.com

Data Engineering3 / 5

An architect is designing a pipeline where a dynamic table chain (DT1 -> DT2 -> DT3) should target DT3 data freshness within 10 minutes. DT1 sources from a base table, and each transformation adds approximately 2 minutes of processing time. How should TARGET_LAG be configured?

AlexFull explanation from Alex

TARGET_LAG = DOWNSTREAM means a dynamic table has no independent refresh schedule and refreshes only when a downstream dynamic table that reads from it triggers a refresh. In a chain where DT3 is the consumer with the freshness target, DT1 and DT2 should be set to DOWNSTREAM and DT3 should be set to the desired duration. Snowflake measures target lag relative to the base tables at the root of the pipeline and actively schedules refreshes to meet the target, but actual lag can exceed the target if refresh duration, warehouse capacity, data volume, or pipeline depth constrain throughput.

Sourcedocs.snowflake.com

Performance Optimization5 / 5

When should a clustering key be added to a Snowflake table?

AlexFull explanation from Alex

Clustering keys should be added to large tables (typically multiple terabytes) where queries consistently filter on specific columns but partition pruning is poor due to data ordering. Small tables do not benefit from clustering because they have few micro-partitions. The overhead of automatic reclustering makes it cost-effective only for large, frequently queried tables. Per Snowflake docs, clustering keys are best for very large tables (multi-TB) where natural clustering has degraded.

Sourcedocs.snowflake.com

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Conversation preview · SF-ARC

Tag-based masking

You
How can the same masking policy follow a classification tag?
Alex · AI tutor
Associate the masking policy with the tag so it applies to matching tagged columns.
Read the Snowflake documentation ↗
You
Does every column need its own separately assigned policy?
Alex · AI tutor
Tag-based masking lets the tag drive the policy application for those columns.
Read the Snowflake documentation ↗

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SF-ARC · Question excerpt

Tag-based masking

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What is the purpose of tag-based masking in Snowflake?

Understand the correct answer.

To automatically apply masking policies to columns with a specific tag

Tag-based masking applies a masking policy through the column’s tag.

Read the Snowflake documentation ↗

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Questions: 65
Minutes: 115

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Question counts and mock exam settings from the current Pass-IT catalog.

Exam domains

What you practice for SF-ARC.

Exam topics, relevant practice and sources you can check.

Design accounts and securitySee the guide for weighting98 QuestionsPass-IT questions

Turn business, privacy and access requirements into a Snowflake architecture.

Choose one or several accounts and configure account, object and session parameters with their inheritance rules.
Design role hierarchies, storage access and governance using secure views, masking, tokenization, row, aggregation and projection policies, tags and lineage; account for edition and compliance requirements.
Combine encryption, network rules, private connectivity and controlled external access with user provisioning, authentication policies, SSO, OAuth, MFA and key pairs.

Official Snowflake objectives

Shape the data architectureSee the guide for weighting120 QuestionsPass-IT questions

Design the data model, object structure and operating lifecycle together.

Compare Data Vault and star schemas, and understand how key and column constraints affect the model.
Design secure data sharing across accounts, regions and clouds or with non-Snowflake consumers, using Marketplace, Data Exchange, Clean Rooms and cross-cloud fulfillment as appropriate.
Plan lake and warehouse layers, ETL or ELT workloads, development environments, migration, Git, CI/CD and rollback; place Snowpark containers, ML, Cortex, Streamlit and Native Apps in AI application designs.
Arrange databases, schemas, tables, views, stages, file formats, warehouses, roles, functions, procedures, streams and tasks within the object hierarchy.
Choose Time Travel, cloning, Fail-safe, replication or failover for recovery, considering table types, corruption, cost and availability.

Official Snowflake objectives

Design data pipelinesSee the guide for weighting103 QuestionsPass-IT questions

Choose how data enters, changes and leaves Snowflake.

Select bulk loads, Snowpipe, streaming, CDC, external or Iceberg tables for the source; handle reloads, incremental changes, schema evolution, loading errors and unloading.
Choose connectors, JDBC or ODBC drivers, SQL API endpoints, SnowSQL, Snowflake CLI or Snowpark to connect applications and tools.
Design transformations with staging layers, tables and views, dynamic tables, FLATTEN, procedures, streams, tasks, UDFs, UDTFs and external or secure functions, considering cost and performance.

Official Snowflake objectives

Diagnose architectural bottlenecksSee the guide for weighting81 QuestionsPass-IT questions

Use workload evidence to choose effective performance changes.

Interpret Query Profiles, queueing and spilling, then adjust warehouse size, multi-cluster scaling, suspension or query acceleration, including Snowpark-optimized warehouses.
Choose clustering keys, automatic clustering, search optimization and cache strategies with their expiry and cost implications.
Troubleshoot pruning and resource use with clustering information, warehouse monitoring, usage views, resource monitors, alerts and event-table logs and traces.

Official Snowflake objectives

Question formats you can practice

Multiple Choice: Select one answer.
Multiple Response: Select multiple applicable answers.

These are Pass-IT practice formats. Check the Snowflake certification page for the official exam information.

How we review questions

Source check: The answer, answer options and explanation are checked against official documentation. We call this canonical validation.

Adversarial review: An additional AI reviewer specifically looks for factual errors, ambiguous answers and weaknesses in the explanation.

Learning objectives: We count specific exam topics with clearly linked exercises. Subheadings are not counted separately.

The figures refer to available questions in each SF-ARC exam domain. The additional review count includes matching review logs for the current question version, even if errors were found. It is not an error-free rate.

The date shows the latest matching evidence or log entry in each domain. An import or transfer of an existing review result can update this entry. This does not mean the entire question pool has been reviewed again.

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Frequently asked questions

Your preparation

How much study time should I plan for SF-ARC?

For planning, Pass-IT estimates roughly 50–120 hours. Relevant experience may put you toward the lower end; starting from scratch may put you toward the higher end. Your needs may fall outside this range. This is not a time requirement from Snowflake or a promise that you will be exam-ready within that time.

What do you need before the Advanced Architect exam?

SnowPro Core certification is required, not recommended. Snowflake additionally expects two or more years of hands-on architect work in production, and the catalog budget is around 80 hours. Without the Core certification you cannot book this exam at all.

Using Pass-IT

What language are Pass-IT questions in?

Questions in the app are currently in English. Translations of the sample questions on this page apply only to this preview. Check with Snowflake for the languages available for the official exam.

How long does my SF-ARC access last?

Access covers this certification for 12 months. You pay once, with no automatic renewal.

When does the money-back guarantee apply?

The guarantee refunds the Pass-IT purchase price for this certification. You need eligible paid access and a Readiness Score of at least 80% from your practice results when you take the exam. This score can only reach 80% after at least 50 answered questions and is not a validated probability of passing. Take the exam during your access period and submit official proof of failing within 14 days. Guarantee terms

About the SF-ARC exam

Where can I take the SF-ARC exam?

Online proctored or onsite testing centers.

How long is the SnowPro Advanced: Architect (ARA-C01) exam?

The SnowPro Advanced: Architect (ARA-C01) exam has 65 questions and a 115-minute time limit.

Advanced Architect or Advanced Administrator?

The administrator exam is weighted 31% towards access control and 20% towards performance tuning, which is the running-it job. The architect exam puts 30% on architecture and 25% on data engineering, which is the designing-it job. Both require SnowPro Core first, and neither is a level above the other.

How many attempts do you get at the Advanced Architect exam?

You get at most four attempts at the Advanced Architect exam in any 12-month period. You must wait seven days between all attempts, including before your first retake.

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