ARA-C01 Exam Dumps - SnowPro Advanced: Architect Certification Exam

This question tests several core Snowflake semi-structured data concepts that are explicitly part of the SnowPro Architect exam scope: working with VARIANT data, array handling, and the use of LATERAL FLATTEN. The activities element in the JSON structure is an array, meaning it must be flattened before individual attributes such as activityNumber and winner can be accessed. Option A is invalid because it attempts to directly reference fields inside an array without flattening it.

Option B correctly uses LATERAL FLATTEN on json:activities, which produces one row per activity. The alias p.value represents each array element, allowing access to activityNumber and winner. The IFF expression then compares the activity’s winner value with leader.number. When they match, the query returns leader.name.default; otherwise, it returns follower.name.default. Casting the result to VARCHAR ensures a proper scalar output.

Option C is incorrect because it attempts to return full JSON objects (leader or follower) rather than the nested name.default value. Option D uses OUTER => TRUE, which is unnecessary in this case because the activities array is guaranteed to exist; while it would still work, Snowflake exam questions typically expect the most precise and minimal correct solution.

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.

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

When using the Snowflake Connector for Kafka, architects must understand the behavior differences between Snowpipe (file-based) and Snowpipe Streaming. Snowpipe Streaming is optimized for low-latency ingestion and works by continuously sending records directly into Snowflake-managed channels rather than staging files. One important characteristic is that Snowpipe Streaming automatically flushes buffered records at short, fixed intervals (approximately every second), ensuring near real-time data availability (Answer D).

Another key consideration is offset handling. The Snowflake Connector for Kafka is designed to tolerate Kafka offset jumps or resets, such as those caused by topic reprocessing or consumer group changes. Snowflake can safely ingest records without corrupting state, relying on Kafka semantics and connector metadata to maintain consistency (Answer E).

Snowpipe Streaming is not always the default ingestion method; configuration determines whether file-based Snowpipe or Streaming is used. Schema detection is not supported in Snowpipe Streaming. Traditional Snowpipe does not offer lower latency than Snowpipe Streaming. For the SnowPro Architect exam, understanding ingestion latency, buffering behavior, and fault tolerance is essential when designing streaming architectures.

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QUESTION NO: 57 [Snowflake Data Engineering]

An Architect wants to create an externally managed Iceberg table in Snowflake.

What parameters are required? (Select THREE).

A. External volume

B. Storage integration

C. External stage

D. Data file path

E. Catalog integration

F. Metadata file path

Answer: A, E, F

Externally managed Iceberg tables in Snowflake rely on external systems for metadata and storage management. An external volume is required to define and manage access to the underlying cloud storage where the Iceberg data files reside (Answer A). A catalog integration is required so Snowflake can interact with the external Iceberg catalog (such as AWS Glue or other supported catalogs) that manages table metadata (Answer E).

Additionally, Snowflake must know the location of the Iceberg metadata files (the Iceberg metadata JSON), which is provided via the metadata file path parameter (Answer F). This allows Snowflake to read schema and snapshot information maintained externally.

An external stage is not required for Iceberg tables, as Snowflake accesses the data directly through the external volume. A storage integration is used for stages, not for Iceberg tables. The data file path is derived from metadata and does not need to be specified explicitly. This question tests SnowPro Architect understanding of modern open table formats and Snowflake’s Iceberg integration model.

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QUESTION NO: 58 [Security and Access Management]

A company stores customer data in Snowflake and must protect Personally Identifiable Information (PII) to meet strict regulatory requirements.

What should an Architect do?

A. Use row-level security to mask PII data.

B. Use tag-based masking policies for columns containing PII.

C. Create secure views for PII data and grant access as needed.

D. Separate PII into different tables and grant access as needed.

Answer: B

Tag-based masking policies provide a scalable and centralized way to protect PII across many tables and schemas (Answer B). By tagging columns that contain PII and associating masking policies with those tags, Snowflake automatically enforces masking rules wherever the tagged columns appear. This approach reduces administrative overhead and ensures consistent enforcement as schemas evolve.

Row access policies control row visibility, not column masking. Secure views and table separation can protect data but introduce significant maintenance complexity and do not scale well across large environments. Snowflake best practices—and the SnowPro Architect exam—emphasize tag-based governance for sensitive data.

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QUESTION NO: 59 [Security and Access Management]

An Architect created a data share and wants to verify that only specific records in secure views are visible to consumers.

What is the recommended validation method?

A. Create reader accounts and log in as consumers.

B. Create a row access policy and assign it to the share.

C. Set the SIMULATED_DATA_SHARING_CONSUMER session parameter.

D. Alter the share to impersonate a consumer account.

Answer: C

Snowflake provides the SIMULATED_DATA_SHARING_CONSUMER session parameter to allow providers to test how shared data appears to specific consumer accounts without logging in as those consumers (Answer C). This feature enables secure, efficient validation of row-level and column-level filtering logic implemented through secure views.

Creating reader accounts is unnecessary and operationally heavy. Row access policies are part of access control design, not validation. Altering a share does not provide impersonation capabilities. This question tests SnowPro Architect familiarity with governance validation tools in Secure Data Sharing scenarios.

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QUESTION NO: 60 [Architecting Snowflake Solutions]

Which requirements indicate that a multi-account Snowflake strategy should be used? (Select TWO).

A. A requirement to use different Snowflake editions.

B. A requirement for easy object promotion using zero-copy cloning.

C. A requirement to use Snowflake in a single cloud or region.

D. A requirement to minimize complexity of changing database names across environments.

E. A requirement to use RBAC to govern DevOps processes across environments.

Answer: A, B

A multi-account Snowflake strategy is appropriate when environments have fundamentally different requirements. Using different Snowflake editions (for example, Business Critical for production and Enterprise for non-production) requires separate accounts because edition is an account-level property (Answer A).

Zero-copy cloning is frequently used for fast environment refresh and object promotion, but cloning only works within a single account. To promote data between environments cleanly, many organizations use separate accounts combined with replication or sharing strategies, making multi-account design relevant when environment isolation and promotion workflows are required (Answer B).

Single-region usage, minimizing database name changes, and RBAC governance can all be handled within a single account. This question reinforces SnowPro Architect principles around environment isolation, governance, and account-level design decisions.

According to the Principle of Least Privilege, the Architect should grant the minimum privileges necessary for the USER1 to find the tables in the SANDBOX database.

The USER1 needs to have USAGE privilege on the SANDBOX database and the SANDBOX.PUBLIC schema to be able to access the tables in the PUBLIC schema. Therefore, the commands B and C are the correct ones to run.

The command A is not correct because the PUBLIC role is automatically granted to every user and role in the account, and it does not have any privileges on the SANDBOX database by default.

The command D is not correct because it would transfer the ownership of the SANDBOX database from the Architect to the USER1, which is not necessary and violates the Principle of Least Privilege.

The command E is not correct because it would grant all the possible privileges on the SANDBOX database to the USER1, which is also not necessary and violates the Principle of Least Privilege.

Snowflake - Principle of Least Privilege : Snowflake - Access Control Privileges : Snowflake - Public Role : Snowflake - Ownership and Grants

Snowpark workloads are often memory- and compute-intensive, especially when executing complex transformations, large joins, or machine learning logic inside stored procedures. In Snowflake, the MAX_CONCURRENCY_LEVEL warehouse parameter controls how many concurrent queries can run on a single cluster of a virtual warehouse. Lowering concurrency increases the amount of compute and memory available to each individual query.

Setting MAX_CONCURRENCY_LEVEL = 1 ensures that only one query can execute at a time on the warehouse cluster, allowing that query to consume the maximum possible share of CPU, memory, and I/O resources. This is the recommended configuration when the goal is to optimize performance for a single Snowpark job rather than maximizing throughput for many users. Higher concurrency levels would divide resources across multiple queries, reducing per-query performance and potentially causing spilling to remote storage.

For SnowPro Architect candidates, this question reinforces an important cost and performance tradeoff: concurrency tuning is a powerful lever. When running batch-oriented or compute-heavy Snowpark workloads, architects should favor lower concurrency to maximize per-query resources, even if that means fewer concurrent workloads.

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QUESTION NO: 12 [Cost Control and Resource Management]

An Architect executes the following query:

SELECT query_hash,

COUNT(*) AS query_count,

SUM(QH.EXECUTION_TIME) AS total_execution_time,

SUM((QH.EXECUTION_TIME / (1000 * 60 * 60)) * 8) AS c

FROM SNOWFLAKE.ACCOUNT_USAGE.QUERY_HISTORY QH

WHERE warehouse_name = 'WH_L'

AND DATE_TRUNC('day', start_time) >= CURRENT_DATE() - 3

GROUP BY query_hash

ORDER BY c DESC

LIMIT 10;

What information does this query provide? (Select TWO).

A. It shows the total execution time and credit estimates for the 10 most expensive individual queries executed on WH_L over the last 3 days.

B. It shows the total execution time and credit estimates for the 10 most expensive query groups (identical or similar queries) executed on WH_L over the last 3 days.

C. It shows the total execution time and credit estimates for the 10 most frequently run query groups executed on WH_L over the last 3 days.

D. It calculates relative cost by converting execution time to minutes and multiplying by credits used.

E. It calculates relative cost by converting execution time to hours and multiplying by credits used.

Answer: B, E

This query groups results by QUERY_HASH, which represents logically identical SQL statements. As a result, the aggregation is performed at the query group level, not at the individual execution level. This allows architects to identify patterns where the same query (or same logical SQL) repeatedly consumes a large amount of compute (Answer B).

The cost calculation converts execution time from milliseconds to hours by dividing by (1000 * 60 * 60) and then multiplies the result by 8, which represents the hourly credit consumption of the WH_L warehouse size. This provides a relative estimate of credit usage per query group, not an exact billing value but a useful approximation for cost analysis (Answer E).

The query does not identify the most frequently executed queries; although COUNT(*) is included, the ordering is done by calculated cost (c), not by frequency. This type of analysis is directly aligned with SnowPro Architect responsibilities, helping architects optimize workloads, refactor expensive query patterns, and right-size warehouses to control costs.

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QUESTION NO: 13 [Architecting Snowflake Solutions]

An Architect is designing a disaster recovery plan for a global fraud reporting system. The plan must support near real-time systems using Snowflake data, operate near regional centers with fully redundant failover, and must not be publicly accessible.

Which steps must the Architect take? (Select THREE).

A. Create multiple replicating Snowflake Standard edition accounts.

B. Establish one Snowflake account using a Business Critical edition or higher.

C. Establish multiple Snowflake accounts in each required region with independent data sets.

D. Set up Secure Data Sharing among all Snowflake accounts in the organization.

E. Create a Snowflake connection object.

F. Create a failover group for the fraud data for each regional account.

Answer: B, C, F

Mission-critical, near real-time systems with strict availability and security requirements require advanced Snowflake features. Business Critical edition (or higher) is required to support failover groups and cross-region replication with higher SLA guarantees and compliance capabilities (Answer B). To meet regional proximity and redundancy requirements, multiple Snowflake accounts must be deployed in each required region, ensuring independence and isolation between regional environments (Answer C).

Failover groups are the core Snowflake mechanism for disaster recovery. They replicate selected databases, schemas, and roles across accounts and allow controlled promotion of secondary accounts to primary during failover events (Answer F). Secure Data Sharing alone does not provide DR or replication, and connection objects are unrelated to availability or redundancy.

This design aligns with SnowPro Architect best practices for multi-region disaster recovery, enabling low-latency regional access, controlled failover, and strong isolation without exposing systems to the public internet.

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QUESTION NO: 14 [Snowflake Data Engineering]

What transformations are supported in the following SQL statement? (Select THREE).

CREATE PIPE … AS

COPY INTO …

FROM ( … )

A. Data can be filtered by an optional WHERE clause.

B. Columns can be reordered.

C. Columns can be omitted.

D. Type casts are supported.

E. Incoming data can be joined with other tables.

F. The ON_ERROR = ABORT_STATEMENT command can be used.

Answer: A, B, D

Snowflake’s COPY INTO statement (including when used with Snowpipe) supports a limited but useful set of transformations. Data can be filtered using a WHERE clause when loading from a staged SELECT statement, enabling simple row-level filtering (Answer A). Columns can also be reordered by explicitly selecting fields in a different order than they appear in the source (Answer B). Additionally, type casting is supported, allowing raw data to be cast into target column data types during ingestion (Answer D).

However, COPY INTO does not support joins with other tables; it is designed for ingestion, not complex transformations. Columns can be omitted implicitly by not selecting them, but this is not considered a transformation feature in the context of Snowpipe exam questions. The ON_ERROR option is an error-handling configuration, not a transformation.

SnowPro Architect candidates are expected to recognize that COPY INTO and Snowpipe are ingestion-focused tools. More complex transformations should be handled downstream using streams and tasks, dynamic tables, or transformation frameworks like dbt.

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QUESTION NO: 15 [Security and Access Management]

A company wants to share selected product and sales tables with global partners. The partners are not Snowflake customers but do have access to AWS.

Requirements:

Data access must be governed.

Each partner should only have access to data from its respective region.What is the MOST secure and cost-effective solution?

A. Create reader accounts and share custom secure views.

B. Create an outbound share and share custom secure views.

C. Export secure views to each partner’s Amazon S3 bucket.

D. Publish secure views on the Snowflake Marketplace.

Answer: A

When sharing data with partners who are not Snowflake customers, Snowflake reader accounts provide the most secure and cost-effective solution. Reader accounts allow data providers to host and govern access within their own Snowflake environment while allowing consumers to query shared data without owning a Snowflake account (Answer A). This ensures strong governance, centralized billing, and no data movement.

By sharing custom secure views, the company can enforce row-level and column-level security so that each partner only sees data from its authorized region. Outbound shares require the consumer to have their own Snowflake account, which is not the case here. Exporting data to S3 introduces unnecessary data duplication, security risk, and operational overhead. Snowflake Marketplace is designed for broad distribution, not partner-specific regional restrictions.

For the SnowPro Architect exam, this question highlights best practices in secure data sharing, governance, and cost control when collaborating with external, non-Snowflake partners.

When a SnowflakeBusiness Critical (BC)edition account shares data, it must followdata sharing restrictionsdesigned to maintain thehigher level of compliance and securityguaranteed by BC.

Bydefault, BC accounts canonly share data with other BC (or higher)accounts, to maintain consistent security and compliance (e.g., HIPAA, HITRUST, FedRAMP).

However,an exceptioncan be madeif a user with the proper privilegeexplicitly disables the restriction.

Key Concept: share_restrictions

Snowflake enforcesdata sharing restrictionsby default forBC accounts.

Arole with the OVERRIDE SHARE RESTRICTIONS global privilegecan bypass this by setting theshare_restrictions = FALSEwhen adding the target account.

Correct Option: D

This is correct because:

The usermust have a role with the OVERRIDE SHARE RESTRICTIONS privilege.

That user can thenset share_restrictions = FALSEwhen adding the Enterprise edition consumer account.

Official Documentation Extract:

"If the data provider is a Business Critical (or higher) account, Snowflake enforces a restriction by default that only allows sharing data with other Business Critical (or higher) accounts. A user in the provider account with a role that has the global privilege OVERRIDE SHARE RESTRICTIONS can override this restriction by explicitly setting SHARE_RESTRICTIONS = FALSE when adding the consumer account."

Source:Snowflake Docs – CREATE SHARE

Why Other Options Are Incorrect:

A.Incorrect – This is not absolute. Business Critical accountscanshare data with Enterprise accounts,if the restriction is explicitly overridden.

B.Incorrect – Simply having authority to create or alter a share isnot enough. You must have the OVERRIDE SHARE RESTRICTIONS privilege and set the restriction explicitly.

C.Incorrect – Setting share_restrictions = FALSE is required, but theprivilege to overridemust also be held by the role. Without the privilege, the action will fail.

https://docs.snowflake.com/en/user-guide/data-sharing-intro