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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Certification Exam is a globally recognized certification program that validates an individual's expertise in designing and implementing Snowflake solutions. SnowPro Advanced Architect Certification certification is designed for experienced architects who have already achieved the SnowPro Core Certification and have a deep understanding of Snowflake's data warehousing platform. The Snowflake ARA-C01 exam covers a wide range of advanced topics, including Snowflake architecture, query optimization, data modeling, security, and performance tuning.

Snowflake ARA-C01 Exam is intended for experienced Snowflake architects, engineers, and developers who want to demonstrate their proficiency in designing and implementing Snowflake solutions. ARA-C01 exam is rigorous and requires candidates to have a deep understanding of Snowflake architecture, design principles, and best practices. It is also recommended that candidates have hands-on experience with Snowflake and are familiar with its various features and functionalities.

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Passing the SnowPro Advanced Architect Certification (ARA-C01) certification is crucial for those who want to excel in the Snowflake industry. However, one of the biggest challenges that individuals face after deciding to take the SnowPro Advanced Architect Certification (ARA-C01) exam is finding authentic ARA-C01 questions for efficient preparation. Those who do not study with real SnowPro Advanced Architect Certification (ARA-C01) dumps often fail the test and waste their valuable resources.

Snowflake ARA-C01 Exam is a timed, multiple-choice exam that contains 75 questions. Candidates have 120 minutes to complete the exam, and they must score at least 70% to pass. ARA-C01 exam is available in English, Japanese, and Spanish, and can be taken online from anywhere in the world. Successful candidates will receive a digital badge and certificate, which they can use to showcase their expertise in Snowflake architecture.

Snowflake SnowPro Advanced Architect Certification Sample Questions (Q114-Q119):

NEW QUESTION # 114
An Architect has chosen to separate their Snowflake Production and QA environments using two separate Snowflake accounts.
The QA account is intended to run and test changes on data and database objects before pushing those changes to the Production account. It is a requirement that all database objects and data in the QA account need to be an exact copy of the database objects, including privileges and data in the Production account on at least a nightly basis.
Which is the LEAST complex approach to use to populate the QA account with the Production account's data and database objects on a nightly basis?

A. 1) Create a stage in the Production account
2) Create a stage in the QA account that points to the same external object-storage location
3) Create a task that runs nightly to unload each table in the Production account into the stage
4) Use Snowpipe to populate the QA account
B. 1) Create a share in the Production account for each database
2) Share access to the QA account as a Consumer
3) The QA account creates a database directly from each share
4) Create clones of those databases on a nightly basis
5) Run tests directly on those cloned databases
C. 1) In the Production account, create an external function that connects into the QA account and returns all the data for one specific table
2) Run the external function as part of a stored procedure that loops through each table in the Production account and populates each table in the QA account
D. 1) Enable replication for each database in the Production account
2) Create replica databases in the QA account
3) Create clones of the replica databases on a nightly basis
4) Run tests directly on those cloned databases

Answer: D

Explanation:
This approach is the least complex because it uses Snowflake's built-in replication feature to copy the data and database objects from the Production account to the QA account. Replication is a fast and efficient way to synchronize data across accounts, regions, and cloud platforms. It also preserves the privileges and metadata of the replicated objects. By creating clones of the replica databases, the QA account can run tests on the cloned data without affecting the original data. Clones are also zero-copy, meaning they do not consume any additional storage space unless the data is modified. This approach does not require any external stages, tasks, Snowpipe, or external functions, which can add complexity and overhead to the data transfer process.
Reference:
Introduction to Replication and Failover
Replicating Databases Across Multiple Accounts
Cloning Considerations

NEW QUESTION # 115
Why might a Snowflake Architect use a star schema model rather than a 3NF model when designing a data architecture to run in Snowflake? (Select TWO).

A. The Bl tool needs a data model that allows users to summarize facts across different dimensions, or to drill down from the summaries.
B. Snowflake cannot handle the joins implied in a 3NF data model.
C. The Architect is designing a landing zone to receive raw data into Snowflake.
D. The Architect wants to remove data duplication from the data stored in Snowflake.
E. The Architect wants to present a simple flattened single view of the data to a particular group of end users.

Answer: A,E

Explanation:
A star schema model is a type of dimensional data model that consists of a single fact table and multiple dimension tables. A 3NF model is a type of relational data model that follows the third normal form, which eliminates data redundancy and ensures referential integrity. A Snowflake Architect might use a star schema model rather than a 3NF model when designing a data architecture to run in Snowflake for the following reasons:
A star schema model is more suitable for analytical queries that require aggregating and slicing data across different dimensions, such as those performed by a BI tool. A 3NF model is more suitable for transactional queries that require inserting, updating, and deleting individual records.
A star schema model is simpler and faster to query than a 3NF model, as it involves fewer joins and less complex SQL statements. A 3NF model is more complex and slower to query, as it involves more joins and more complex SQL statements.
A star schema model can provide a simple flattened single view of the data to a particular group of end users, such as business analysts or data scientists, who need to explore and visualize the data. A 3NF model can provide a more detailed and normalized view of the data to a different group of end users, such as application developers or data engineers, who need to maintain and update the data.
The other options are not valid reasons for choosing a star schema model over a 3NF model in Snowflake:
Snowflake can handle the joins implied in a 3NF data model, as it supports ANSI SQL and has a powerful query engine that can optimize and execute complex queries efficiently.
The Architect can use both star schema and 3NF models to remove data duplication from the data stored in Snowflake, as both models can enforce data integrity and avoid data anomalies. However, the trade-off is that a star schema model may have more data redundancy than a 3NF model, as it denormalizes the data for faster query performance, while a 3NF model may have less data redundancy than a star schema model, as it normalizes the data for easier data maintenance.
The Architect can use both star schema and 3NF models to design a landing zone to receive raw data into Snowflake, as both models can accommodate different types of data sources and formats. However, the choice of the model may depend on the purpose and scope of the landing zone, such as whether it is a temporary or permanent storage, whether it is a staging area or a data lake, and whether it is a single source or a multi-source integration.
Snowflake Architect Training
Data Modeling: Understanding the Star and Snowflake Schemas
Data Vault vs Star Schema vs Third Normal Form: Which Data Model to Use?
Star Schema vs Snowflake Schema: 5 Key Differences
Dimensional Data Modeling - Snowflake schema
Star schema vs Snowflake Schema

NEW QUESTION # 116
A Snowflake Architect is designing an application and tenancy strategy for an organization where strong legal isolation rules as well as multi-tenancy are requirements.
Which approach will meet these requirements if Role-Based Access Policies (RBAC) is a viable option for isolating tenants?

A. Create an object for each tenant strategy if row level security is viable for isolating tenants.
B. Create accounts for each tenant in the Snowflake organization.
C. Create an object for each tenant strategy if row level security is not viable for isolating tenants.
D. Create a multi-tenant table strategy if row level security is not viable for isolating tenants.

Answer: B

NEW QUESTION # 117
An Architect has been asked to clone schema STAGING as it looked one week ago, Tuesday June 1st at 8:00 AM, to recover some objects.
The STAGING schema has 50 days of retention.
The Architect runs the following statement:
CREATE SCHEMA STAGING_CLONE CLONE STAGING at (timestamp => '2021-06-01 08:00:00'); The Architect receives the following error: Time travel data is not available for schema STAGING. The requested time is either beyond the allowed time travel period or before the object creation time.
The Architect then checks the schema history and sees the following:
CREATED_ON|NAME|DROPPED_ON
2021-06-02 23:00:00 | STAGING | NULL
2021-05-01 10:00:00 | STAGING | 2021-06-02 23:00:00
How can cloning the STAGING schema be achieved?

A. Rename the STAGING schema and perform an UNDROP to retrieve the previous STAGING schema version, then run the CLONE statement.
B. Modify the statement: CREATE SCHEMA STAGING_CLONE CLONE STAGING at (timestamp =>
'2021-05-01 10:00:00');
C. Undrop the STAGING schema and then rerun the CLONE statement.
D. Cloning cannot be accomplished because the STAGING schema version was not active during the proposed Time Travel time period.

Answer: D

Explanation:
The error encountered during the cloning attempt arises because the schema STAGING as it existed on June
1st, 2021, is not within the Time Travel retention period. According to the schema history, STAGING was recreated on June 2nd, 2021, after being dropped on the same day. The requested timestamp of '2021-06-01
08:00:00' is prior to this recreation, hence not available. The STAGING schema from before June 2nd was dropped and exceeded the Time Travel period for retrieval by the time of the cloning attempt. Therefore, cloning STAGING as it looked on June 1st, 2021, cannot be achieved because the data from that time is no longer available within the allowed Time Travel window.References: Snowflake documentation on Time Travel and data cloning, which is covered under the SnowPro Advanced: Architect certification resources.

NEW QUESTION # 118
A Data Engineer is designing a near real-time ingestion pipeline for a retail company to ingest event logs into Snowflake to derive insights. A Snowflake Architect is asked to define security best practices to configure access control privileges for the data load for auto-ingest to Snowpipe.
What are the MINIMUM object privileges required for the Snowpipe user to execute Snowpipe?

A. OWNERSHIP on the named pipe, USAGE on the named stage, target database, and schema, and INSERT and SELECT on the target table
B. OWNERSHIP on the named pipe, USAGE and READ on the named stage, USAGE on the target database and schema, and INSERT end SELECT on the target table
C. USAGE on the named pipe, named stage, target database, and schema, and INSERT and SELECT on the target table
D. CREATE on the named pipe, USAGE and READ on the named stage, USAGE on the target database and schema, and INSERT end SELECT on the target table

Answer: B

Explanation:
According to the SnowPro Advanced: Architect documents and learning resources, the minimum object privileges required for the Snowpipe user to execute Snowpipe are:
* OWNERSHIP on the named pipe. This privilege allows the Snowpipe user to create, modify, and drop the pipe object that defines the COPY statement for loading data from the stage to the table1.
* USAGE and READ on the named stage. These privileges allow the Snowpipe user to access and read the data files from the stage that are loaded by Snowpipe2.
* USAGE on the target database and schema. These privileges allow the Snowpipe user to access the database and schema that contain the target table3.
* INSERT and SELECT on the target table. These privileges allow the Snowpipe user to insert data into the table and select data from the table4.
The other options are incorrect because they do not specify the minimum object privileges required for the Snowpipe user to execute Snowpipe. Option A is incorrect because it does not include the READ privilege on the named stage, which is required for the Snowpipe user to read the data files from the stage. Option C is incorrect because it does not include the OWNERSHIP privilege on the named pipe, which is required for the Snowpipe user to create, modify, and drop the pipe object. Option D is incorrect because it does not include the OWNERSHIP privilege on the named pipe or the READ privilege on the named stage, which are both required for the Snowpipe user to execute Snowpipe. References: CREATE PIPE | Snowflake Documentation, CREATE STAGE | Snowflake Documentation, CREATE DATABASE | Snowflake Documentation, CREATE TABLE | Snowflake Documentation

NEW QUESTION # 119
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