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

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.

 The warehouse MY_WH will only be active when there are results in the stream. This is because the task is created based on a stream, which means that the task will only be executed when there are new data in the stream. Additionally, the warehouse is set to auto_suspend - 60, which means that the warehouse will automatically suspend after 60 seconds of inactivity. Therefore, the warehouse will only be active when there are results in the stream. References:

[CREATE TASK | Snowflake Documentation]

[Using Streams and Tasks | Snowflake Documentation]

[CREATE WAREHOUSE | Snowflake Documentation]

A. The Search Optimization Service is designed to accelerate the performance of queries that use filters on large tables. One of the key considerations for its effectiveness is using it with tables where the columns used in the filter conditions have a high number of distinct values, typically in the hundreds of thousands or more. This is because the service creates a map-reduce-like index on the column to speed up queries that use point lookups or range scans on that column. The more unique values there are, the more effective the index is at narrowing down the search space.

Comprehensive and Detailed Explanation From Exact Extract:

To understand this, we must differentiate between:

SHOW GRANTS TO <object>

SHOW GRANTS ON <object>

1. SHOW GRANTS TO USER user_01;

This commandlists all the privileges granted to the useruser_01. These can include:

Roles granted to the user

Global privileges such as MONITOR, USAGE, etc.

🔹Official Documentation Extract:

"SHOW GRANTS TO USERdisplays the roles and other privileges that have been granted to the specified user."

Source: Snowflake Docs – SHOW GRANTS TO

According to the SnowPro Advanced: Architect documents and learning resources, column masking policies are applied at query time based on the privileges of the user who runs the query. Therefore, if a user has the privilege to see unmasked data in a column, they will see the original data when they query that column. If they load this column data into another column that does not have a masking policy, the unmasked data will be loaded in the new column, and any user who can query the new column will see the unmasked data as well. The masking policy does not affect the underlying data in the column, only the query results.

Snowflake Documentation: Column Masking

Snowflake Learning: Column Masking

This design meets all the requirements for the data pipeline. Snowpipe is a feature that enables continuous data loading into Snowflake from object storage using event notifications. It is efficient, scalable, and serverless, meaning it does not require any infrastructure or maintenance from the user. Streams and tasks are features that enable automated data pipelines within Snowflake, using change data capture and scheduled execution. They are also efficient, scalable, and serverless, and they simplify the data transformation process. External functions are functions that can invoke external services or APIs from within Snowflake. They can be used to integrate with Amazon Comprehend and perform sentiment analysis on the data. The results can be written back to a Snowflake table using standard SQL commands. Snowflake Marketplace is a platform that allows data providers to share data with data consumers across different accounts, regions, and cloud platforms. It is a secure and easy way to make data publicly available to other companies.

Snowpipe Overview | Snowflake Documentation

Introduction to Data Pipelines | Snowflake Documentation

External Functions Overview | Snowflake Documentation

Snowflake Data Marketplace Overview | Snowflake Documentation

According to the Snowflake documentation1 and the web search results2, these two statements are true about how the change of local time due to daylight savings time is handled in Snowflake tasks. A task is a feature that allows scheduling and executing SQL statements or stored procedures in Snowflake. A task can be scheduled using a cron expression that specifies the frequency and time zone of the task execution.

A task scheduled in a UTC-based schedule will have no issues with the time changes. UTC is a universal time standard that does not observe daylight savings time. Therefore, a task that uses UTC as the time zone will run at the same time throughout the year, regardless of the local time changes1.

Task schedules can be designed to follow specified or local time zones to accommodate the time changes. Snowflake supports using any valid IANA time zone identifier in the cron expression for a task. This allows the task to run according to the local time of the specified time zone, which may include daylight savings time adjustments. For example, a task that uses Europe/London as the time zone will run one hour earlier or later when the local time switches between GMT and BST12.

Snowflake Documentation: Scheduling Tasks

Snowflake Community: Do the timezones used in scheduling tasks in Snowflake adhere to daylight savings?

 Option C is the correct answer because schema-on-read is a technique that allows Snowflake to ingest and consume semi-structured data without requiring a predefined schema. Snowflake supports various semi-structured data formats such as JSON, Avro, ORC, Parquet, and XML, and provides native data types (ARRAY, OBJECT, and VARIANT) for storing them. Snowflake also provides native support for querying semi-structured data using SQL and dot notation. Schema-on-read enables Snowflake to query semi-structured data at the same speed as performing relational queries while preserving the flexibility of schema-on-read. Snowflake’s near-instant elasticity rightsizes compute resources, and consumption-based pricing ensures you only pay for what you use.

Option A is incorrect because IDEF1X is a data modeling technique that defines the structure and constraints of relational data using diagrams and notations. IDEF1X is not suitable for ingesting and consuming semi-structured data, which does not have a fixed schema or structure.

Option B is incorrect because schema-on-write is a technique that requires defining a schema before loading and processing data. Schema-on-write is not efficient for ingesting and consuming semi-structured data, which may have varying or complex structures that are difficult to fit into a predefined schema. Schema-on-write also introduces additional overhead and complexity for data transformation and validation.

Option D is incorrect because information schema is a set of metadata views that provide information about the objects and privileges in a Snowflake database. Information schema is not a technique for ingesting and consuming semi-structured data, but rather a way of accessing metadata about the data.