The best design to store and analyze large amounts of structured and semi-structured data for different business units and departments is to use a centralized Snowflake database for core business data, and use separate databases for departmental or project-specific data. This design allows for scalability, security, and cost efficiency by leveraging Snowflake’s features such as:

Database cloning: Cloning a database creates a zero-copy clone that shares the same data files as the original database, but can be modified independently. This reduces storage costs and enables fast and consistent data replication for different purposes.

Database sharing: Sharing a database allows granting secure and governed access to a subset of data in a database to other Snowflake accounts or consumers. This enables data collaboration and monetization across different business units or external partners.

Warehouse scaling: Scaling a warehouse allows adjusting the size and concurrency of a warehouse to match the performance and cost requirements of different workloads. This enables optimal resource utilization and flexibility for different data analysis needs. References: Snowflake Documentation: Database Cloning, Snowflake Documentation: Database Sharing, [Snowflake Documentation: Warehouse Scaling]

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.

The query is executing a clone operation on an existing tablet_saleswith an offset to account for the retention time. The syntax used is correct for cloning a table in Snowflake, and the use of theat(offset = > -60*30)clause is valid. This specifies that the clone should be based on the state of the table 30 minutes prior (60 seconds * 30). Assuming the tablet_salesexists and has been modified within the last 30 minutes, and considering thedata_retention_time_in_daysis set to 1 day (which enables time travel queries for the past 24 hours), the tablet_sales_clonewould be successfully created based on the state oft_sales30 minutes before the clone command was issued.

Snowflake is a cloud data platform that supports various data models for modeling tables in a Snowflake environment. The data models can be classified into two categories: dimensional and normalized. Dimensional data models are designed to optimize query performance and ease of use for business intelligence and analytics. Normalized data models are designed to reduce data redundancy and ensure data integrity for transactional and operational systems. The following are some of the data models that can be used in Snowflake:

Dimensional/Kimball: This is a popular dimensional data model that uses a star or snowflake schema to organize data into fact and dimension tables. Fact tables store quantitative measures and foreign keys to dimension tables. Dimension tables store descriptive attributes and hierarchies. A star schema has a single denormalized dimension table for each dimension, while a snowflake schema has multiple normalized dimension tables for each dimension. Snowflake supports both star and snowflake schemas, and allows users to create views and joins to simplify queries.

Inmon/3NF: This is a common normalized data model that uses a third normal form (3NF) schema to organize data into entities and relationships. 3NF schema eliminates data duplication and ensures data consistency by applying three rules: 1) every column in a table must depend on the primary key, 2) every column in a table must depend on the whole primary key, not a part of it, and 3) every column in a table must depend only on the primary key, not on other columns. Snowflake supports 3NF schema and allows users to create referential integrity constraints and foreign key relationships to enforce data quality.

Data vault: This is a hybrid data model that combines the best practices of dimensional and normalized data models to create a scalable, flexible, and resilient data warehouse. Data vault schema consists of three types of tables: hubs, links, and satellites. Hubs store business keys and metadata for each entity. Links store associations and relationships between entities. Satellites store descriptive attributes and historical changes for each entity or relationship. Snowflake supports data vault schema and allows users to leverage its features such as time travel, zero-copy cloning, and secure data sharing to implement data vault methodology.

 What is Data Modeling? | Snowflake, Snowflake Schema in Data Warehouse Model - GeeksforGeeks, [Data Vault 2.0 Modeling with 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.

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.

To provide the highest uptime and least application disruption in case of a service event, the best option is to use the Business Critical Snowflake edition and connect the applications using the < organization_name > - < accountLocator > URL. The Business Critical Snowflake edition offers the highest level of security, performance, and availability for Snowflake accounts. It includes features such as customer-managed encryption keys, HIPAA compliance, and 4-hour RPO and RTO SLAs. It also supports account replication and failover across regions and cloud platforms, which enables business continuity and disaster recovery. By using the < organization_name > - < accountLocator > URL, the applications can leverage the Snowflake Client Redirect feature, which automatically redirects the client connections to the secondary account in case of a failover. This way, the applications can seamlessly switch to the backup account without any manual intervention or configuration changes. The other options are less cost-effective or less reliable because they either use a lower edition of Snowflake, which does not support account replication and failover, or they use the < organization_name > - < connection_name > URL, which does not support client redirect and requires manual updates to the connection string in case of a failover. References:

[Snowflake Editions] 1

[Replication and Failover/Failback] 2

[Client Redirect] 3

[Snowflake Account Identifiers] 4

To create a read-only role for certain employees working in the human resources department, the role needs to have the following permissions on the hr_db database:

USAGE on the database: This allows the role to access the database and see its schemas and objects.

USAGE on all schemas in the database: This allows the role to access the schemas and see their objects.

SELECT on all tables in the database: This allows the role to query the data in the tables.

Option A is the correct answer because it grants the minimum permissions required for a read-only role on the hr_db database.

Option B is incorrect because SELECT on schemas is not a valid permission. Schemas only support USAGE and CREATE permissions.

Option C is incorrect because MODIFY on the database is not a valid permission. Databases only support USAGE, CREATE, MONITOR, and OWNERSHIP permissions. Moreover, USAGE on tables is not sufficient for querying the data. Tables support SELECT, INSERT, UPDATE, DELETE, TRUNCATE, REFERENCES, and OWNERSHIP permissions.

Option D is incorrect because REFERENCES on tables is not relevant for querying the data. REFERENCES permission allows the role to create foreign key constraints on the tables.

https://docs.snowflake.com/en/user-guide/security-access-control-privileges.html#database-privileges

https://docs.snowflake.com/en/user-guide/security-access-control-privileges.html#schema-privileges

https://docs.snowflake.com/en/user-guide/security-access-control-privileges.html#table-privileges

 According to the SnowPro Advanced: Architect documents and learning resources, the requirement to allow data sharing between two companies that are not on the same cloud platform is to set up data replication to the region and cloud platform where the consumer resides. Data replication is a feature of Snowflake that enables copying databases across accounts in different regions and cloud platforms. Data replication allows data providers to securely share data with data consumers across different regions and cloud platforms by creating a replica database in the consumer’s account. The replica database is read-only and automatically synchronized with the primary database in the provider’s account. Data replication is useful for scenarios where data sharing is not possible or desirable due to latency, compliance, or security reasons1. The other options are incorrect because they are not required or feasible to allow data sharing between two companies that are not on the same cloud platform. Option A is incorrect because creating a pipeline to write shared data to a cloud storage location in the target cloud provider is not a secure or efficient way of sharing data. It would require additional steps to load the data from the cloud storage to the consumer’s account, and it would not leverage the benefits of Snowflake’s data sharing features. Option B is incorrect because ensuring that all views are persisted is not relevant for data sharing across cloud platforms. Views can be shared across cloud platforms as long as they reference objects in the same database. Persisting views is an option to improve the performance of querying views, but it is notrequired for data sharing2. Option D is incorrect because Company A and Company B do not need to agree to use a single cloud platform. Data sharing is possible across different cloud platforms using data replication or other methods, such as listings or auto-fulfillment3. References: ReplicatingDatabases Across Multiple Accounts | Snowflake Documentation, Persisting Views | Snowflake Documentation, Sharing Data Across Regions and Cloud Platforms | Snowflake Documentation

The correct answer is A because it improves the performance of queries by reducing the amount of data scanned and processed. By adding a create_date field with a timestamp data type, Snowflake can automatically cluster the table based on this field and prune the micro-partitions that do not match the filter condition. This avoids the need to parse the JSON data and access the variant field for every record.

Option B is incorrect because it does not improve the performance of queries. By adding a create_date field with a varchar data type, Snowflake cannot automatically cluster the table based on this field and prune the micro-partitions that do not match the filter condition. This still requires parsing the JSON data and accessing the variant field for every record.

Option C is incorrect because it does not address the root cause of the performance issue. By validating the size of the warehouse being used, Snowflake can adjust the compute resources to match the data volume and parallelize the query execution. However, this does not reduce the amount of data scanned and processed, which is the main bottleneck for queries on JSON data.

Option D is incorrect because it adds unnecessary complexity and overhead to the data loading and querying process. By incorporating the use of multiple tables partitioned by date ranges, Snowflake can reduce the amount of data scanned and processed for queries that specify a date range. However, this requires creating and maintaining multiple tables, loading data into the appropriate table based on the date, and joining the tables for queries that span multiple date ranges. References:

Snowflake Documentation: Loading Data Using Snowpipe: This document explains how to use Snowpipe to continuously load data from external sources into Snowflake tables. It also describes the syntax and usage of the COPY INTO command, which supports various options and parameters to control the loading behavior, such as ON_ERROR, PURGE, and SKIP_FILE.

Snowflake Documentation: Date and Time Data Types and Functions: This document explains the different data types and functions for working with date and time values in Snowflake. It also describes how to set and change the session timezone and the system timezone.

Snowflake Documentation: Querying Metadata: This document explains how to query the metadata of the objects and operations in Snowflake using various functions, views, and tables. It also describes how to access the copy history information using the COPY_HISTORY function or the COPY_HISTORY view.

Snowflake Documentation: Loading JSON Data: This document explains how to load JSON data into Snowflake tables using various methods, such as the COPY INTO command, the INSERT command, or the PUT command. It also describes how to access and query JSON data using the dot notation, the FLATTEN function, or the LATERAL join.

Snowflake Documentation: Optimizing Storage for Performance: This document explains how to optimize the storage of data in Snowflake tables to improve the performance of queries. It also describes the concepts and benefits of automatic clustering, search optimization service, and materialized views.