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.

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.

This is the correct answer because it allows the ORDER_ADMIN role to perform the data cleanup without needing the DELETE privilege on the ORDERS table. A stored procedure is a feature that allows scheduling and executing SQL statements or stored procedures in Snowflake. A stored procedure can run with either the caller’s rights or the owner’s rights. A caller’s rights stored procedure runs with the privileges of the role that called the stored procedure, while an owner’s rights stored procedure runs with the privileges of the role that created the stored procedure. By creating a stored procedure that runs with owner’s rights, the ORDER_MANAGER role can delegate the specific task of deleting old data to the ORDER_ADMIN role, without granting the ORDER_ADMIN role more general privileges on the ORDERS table. The stored procedure must include the appropriate business logic to delete only the records older than 5 years, and the ORDER_MANAGER role must grant the USAGE privilege on the stored procedure to the ORDER_ADMIN role. The ORDER_ADMIN role can then execute the stored procedure to perform the data cleanup12.

Snowflake Documentation: Stored Procedures

Snowflake Documentation: Understanding Caller’s Rights and Owner’s Rights Stored Procedures

A securable object is an entity to which access can be granted in Snowflake. Securable objects include databases, schemas, tables, views, stages, pipes, functions, procedures, sequences, tasks, streams, roles, warehouses, and shares1.

The Snowflake object hierarchy is a logical structure that organizes the securable objects in a nested manner. The top-most container is the account, which contains all the databases, roles, and warehouses for the customer organization. Each database contains schemas, which in turn contain tables, views, stages, pipes, functions, procedures, sequences, tasks, and streams. Each role can be granted privileges on other roles or securable objects. Each warehouse can be used to execute queries on securable objects2.

Based on the Snowflake object hierarchy, the securable objects that belong directly to a Snowflake account are databases, roles, and warehouses. These objects are created and managed at the account level, and do not depend on any other securable object. The other options are not correct because:

Schemas belong to databases, not to accounts. A schema must be created within an existing database3.

Tables belong to schemas, not to accounts. A table must be created within an existing schema4.

Stages belong to schemas or tables, not to accounts. A stage must be created within an existing schema or table.

1: Overview of Access Control | Snowflake Documentation

2: Securable Objects | Snowflake Documentation

3: CREATE SCHEMA | Snowflake Documentation

4: CREATE TABLE | Snowflake Documentation

[5]: CREATE STAGE | Snowflake Documentation

According to the SnowPro Advanced: Architect documents and learning resources, the Snowflake functionality that should be used to meet these requirements are:

Use private connectivity from a cloud provider. This feature allows customers to connect to Snowflake from their own private network without exposing their data to the public Internet. Snowflake integrates with AWS PrivateLink, Azure Private Link, and Google Cloud Private Service Connect to offer private connectivity from customers’ VPCs or VNets to Snowflake endpoints. Customers can control how traffic reaches the Snowflake endpoint and avoid the need for proxies or public IP addresses123.

Set up SSO for federated authentication. This feature allows customers to use their existing identity provider (IdP) to authenticate users for SSO access to Snowflake. Snowflake supports most SAML 2.0-compliant vendors as an IdP, including Okta, Microsoft AD FS, Google G Suite, Microsoft Azure Active Directory, OneLogin, Ping Identity, and PingOne. By setting up SSO for federated authentication, customers can leverage their existing user credentials and profile information, and provide stronger security than username/password authentication4.

The other options are incorrect because they do not meet the requirements or are not feasible. Option A is incorrect because setting up replication does not allow users to connect from outside the company VPN. Replication is a feature of Snowflake that enables copying databases across accounts in different regions and cloud platforms. Replication does not affect the connectivity or visibility of the accounts5. Option B is incorrect because provisioning a unique company Tri-Secret Secure key does not affect the network or authentication requirements. Tri-Secret Secure is a feature of Snowflake that allows customers to manage their own encryption keys for data at rest in Snowflake, using a combination of three secrets: a master key, a service key, and a security password. Tri-Secret Secure provides an additional layer of security and control over the data encryption and decryption process, but it does not enable private connectivity or SSO6. Option E is incorrect because using a proxy Snowflake account outside the VPN, enabling client redirect for user logins, is not a supported or recommended way of meeting the requirements. Client redirect is a feature of Snowflake that allows customers to connect to a different Snowflake account than the one specified in the connection string. This feature is useful for scenarios such as cross-region failover, data sharing, and account migration, but it does not provide private connectivity or SSO7. References: AWS PrivateLink & Snowflake | Snowflake Documentation, Azure Private Link & Snowflake | Snowflake Documentation, Google Cloud Private Service Connect & Snowflake | Snowflake Documentation, Overview of Federated Authentication and SSO | Snowflake Documentation, Replicating Databases Across Multiple Accounts | Snowflake Documentation, Tri-Secret Secure | Snowflake Documentation, Redirecting Client Connections | Snowflake Documentation

The best way to address the performance issues and time-outs faced by the Store Manager team is to configure a dedicated virtual warehouse for them and make it multi-clustered. This will allow them to run their reports independently from other workloads and scale up or down the compute resources as needed. A dedicated virtual warehouse will also enable them to apply specific security and access policies for their data. A multi-clustered virtual warehouse will provide high availability and concurrency for their queries and avoid queuing or throttling.

Using a Business Intelligence tool for in-memory computation may improve performance, but it will not solve the underlying issue of insufficient compute resources in the shared virtual warehouse. It will also introduce additional costs and complexity for the data architecture.

Configuring the virtual warehouse to size 4-XL may increase the performance, but it will also increase the cost and may not be optimal for the workload. It will also not address the concurrency and availability issues that may arise from sharing the virtual warehouse with other workloads.

Advising the Store Manager team to defer report execution to off-business hours may reduce the load on the shared virtual warehouse, but it will also reduce the timeliness and usefulness of the reports for the business. It will also not guarantee that the performance issues and time-outs will not occur at other times.