(Oct-2026) ARA-C01 Exam Dumps Contains FREE Real Quesions from the Actual Exam [Q78-Q99]

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(Oct-2026) ARA-C01 Exam Dumps Contains FREE Real Quesions from the Actual Exam

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Snowflake ARA-C01 (SnowPro Advanced Architect Certification) Exam is a certification exam designed for professionals who want to demonstrate their advanced-level skills in Snowflake architecture and design. It is a comprehensive exam that covers various topics such as data modeling, data warehousing, data security, performance optimization, and Snowflake administration.

QUESTION 78
A company has an inbound share set up with eight tables and five secure views. The company plans to make the share part of its production data pipelines.
Which actions can the company take with the inbound share? (Choose two.)

Clone a table from a share.

Grant modify permissions on the share.

Create a table from the shared database.

Create additional views inside the shared database.

Create a table stream on the shared table.

QUESTION 79
Following objects can be cloned in snowflake

Permanent table

Transient table

Temporary table

External tables

Internal stages

QUESTION 80
For the best performance while loading data into table from a stage, you should avoid applying patterns that filter on a large number of files.

FALSE

TRUE

QUESTION 81
A company needs to have the following features available in its Snowflake account:
1. Support for Multi-Factor Authentication (MFA)
2. A minimum of 2 months of Time Travel availability
3. Database replication in between different regions
4. Native support for JDBC and ODBC
5. Customer-managed encryption keys using Tri-Secret Secure
6. Support for Payment Card Industry Data Security Standards (PCI DSS)
In order to provide all the listed services, what is the MINIMUM Snowflake edition that should be selected during account creation?

Standard

Enterprise

Business Critical

Virtual Private Snowflake (VPS)

QUESTION 82
While using joins, non-equality join predicates might result in significantly slower processing speeds and should be avoided if possible.

TRUE

FALSE

QUESTION 83
How can the Snowpipe REST API be used to keep a log of data load history?

Call insertReport every 20 minutes, fetching the last 10,000 entries.

Call loadHistoryScan every minute for the maximum time range.

Call insertReport every 8 minutes for a 10-minute time range.

Call loadHistoryScan every 10 minutes for a 15-minute time range.

QUESTION 84
It is recommended to assign ACCOUNTADMIN role to atleast two user

TRUE

FALSE

QUESTION 85
Which of the below commands will use warehouse credits?

SHOW TABLES LIKE ‘SNOWFL%’;

SELECT MAX(FLAKE_ID) FROM SNOWFLAKE;

SELECT COUNT(*) FROM SNOWFLAKE;

SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID;

QUESTION 86
A global company needs to securely share its sales and Inventory data with a vendor using a Snowflake account.
The company has its Snowflake account In the AWS eu-west 2 Europe (London) region. The vendor’s Snowflake account Is on the Azure platform in the West Europe region. How should the company’s Architect configure the data share?

1. Create a share.2. Add objects to the share.3. Add a consumer account to the share for the vendor to access.

1. Create a share.2. Create a reader account for the vendor to use.3. Add the reader account to the share.

1. Create a new role called db_share.2. Grant the db_share role privileges to read data from the company database and schema.3. Create a user for the vendor.4. Grant the ds_share role to the vendor’s users.

1. Promote an existing database in the company’s local account to primary.2. Replicate the database to Snowflake on Azure in the West-Europe region.3. Create a share and add objects to the share.4. Add a consumer account to the share for the vendor to access.

QUESTION 87
You are a snowflake architect in an organization. The business team came to to deploy an use case which requires you to load some data which they can visualize through tableau. Everyday new data comes in and the old data is no longer required.
What type of table you will use in this case to optimize cost

TRANSIENT

TEMPORARY

PERMANENT

QUESTION 88
A large manufacturing company runs a dozen individual Snowflake accounts across its business divisions. The company wants to increase the level of data sharing to support supply chain optimizations and increase its purchasing leverage with multiple vendors.
The company’s Snowflake Architects need to design a solution that would allow the business divisions to decide what to share, while minimizing the level of effort spent on configuration and management. Most of the company divisions use Snowflake accounts in the same cloud deployments with a few exceptions for European-based divisions.
According to Snowflake recommended best practice, how should these requirements be met?

Migrate the European accounts in the global region and manage shares in a connected graph architecture. Deploy a Data Exchange.

Deploy a Private Data Exchange in combination with data shares for the European accounts.

Deploy to the Snowflake Marketplace making sure that invoker_share() is used in all secure views.

Deploy a Private Data Exchange and use replication to allow European data shares in the Exchange.

Explanation
According to Snowflake recommended best practice, the requirements of the large manufacturing company should be met by deploying a Private Data Exchange in combination with data shares for the European accounts. A Private Data Exchange is a feature of the Snowflake Data Cloud platform that enables secure and governed sharing of data between organizations. It allows Snowflake customers to create their own data hub and invite other parts of their organization or external partners to access and contribute data sets. A Private Data Exchange provides centralized management, granular access control, and data usage metrics for the data shared in the exchange1. A data share is a secure and direct way of sharing data between Snowflake accounts without having to copy or move the data. A data share allows the data provider to grant privileges on selected objects in their account to one or more data consumers in other accounts2. By using a Private Data Exchange in combination with data shares, the company can achieve the following benefits:
* The business divisions can decide what data to share and publish it to the Private Data Exchange, where it can be discovered and accessed by other members of the exchange. This reduces the effort and complexity of managing multiple data sharing relationships and configurations.
* The company can leverage the existing Snowflake accounts in the same cloud deployments to create the Private Data Exchange and invite the members to join. This minimizes the migration and setup costs and leverages the existing Snowflake features and security.
* The company can use data shares to share data with the European accounts that are in different regions or cloud platforms. This allows the company to comply with the regional and regulatory requirements for data sovereignty and privacy, while still enabling data collaboration across the organization.
* The company can use the Snowflake Data Cloud platform to perform data analysis and transformation on the shared data, as well as integrate with other data sources and applications. This enables the company to optimize its supply chain and increase its purchasing leverage with multiple vendors.
The other options are incorrect because they do not meet the requirements or follow the best practices. Option A is incorrect because migrating the European accounts to the global region may violate the data sovereignty and privacy regulations, and deploying a Data Exchange may not provide the level of control and management that the company needs. Option C is incorrect because deploying to the Snowflake Marketplace may expose the company’s data to unwanted consumers, and using invoker_share() in secure views may not provide the desired level of security and governance. Option D is incorrect because using replication to allow European data shares in the Exchange may incur additional costs and complexity, and may not be necessary if data shares can be used instead. References: Private Data Exchange | Snowflake Documentation, Introduction to Secure Data Sharing | Snowflake Documentation

QUESTION 89
Which of the following ingestion methods can be used to load near real-time data by using the messaging services provided by a cloud provider?

Snowflake Connector for Kafka

Snowflake streams

Snowpipe

Spark

QUESTION 90
A company’s Architect needs to find an efficient way to get data from an external partner, who is also a Snowflake user. The current solution is based on daily JSON extracts that are placed on an FTP server and uploaded to Snowflake manually. The files are changed several times each month, and the ingestion process needs to be adapted to accommodate these changes.
What would be the MOST efficient solution?

Ask the partner to create a share and add the company’s account.

Ask the partner to use the data lake export feature and place the data into cloud storage where Snowflake can natively ingest it (schema-on-read).

Keep the current structure but request that the partner stop changing files, instead only appending new files.

Ask the partner to set up a Snowflake reader account and use that account to get the data for ingestion.

The most efficient solution is to ask the partner to create a share and add the company’s account (Option A). This way, the company can access the live data from the partner without any data movement or manual intervention. Snowflake’s secure data sharing feature allows data providers to share selected objects in a database with other Snowflake accounts. The shared data is read-only and does not incur any storage or compute costs for the data consumers. The data consumers can query the shared data directly or create local copies of the shared objects in their own databases. Option B is not efficient because it involves using the data lake export feature, which is intended for exporting data from Snowflake to an external data lake, not for importing data from another Snowflake account. The data lake export feature also requires the data provider to create an external stage on cloud storage and use the COPY INTO <location> command to export the data into parquet files. The data consumer then needs to create an external table or a file format to load the data from the cloud storage into Snowflake. This process can be complex and costly, especially if the data changes frequently. Option C is not efficient because it does not solve the problem of manual data ingestion and adaptation. Keeping the current structure of daily JSON extracts on an FTP server and requesting the partner to stop changing files, instead only appending new files, does not improve the efficiency or reliability of the data ingestion process. The company still needs to upload the data to Snowflake manually and deal with any schema changes or data quality issues. Option D is not efficient because it requires the partner to set up a Snowflake reader account and use that account to get the data for ingestion. A reader account is a special type of account that can only consume data from the provider account that created it. It is intended for data consumers who are not Snowflake customers and do not have a licensing agreement with Snowflake. A reader account is not suitable for data ingestion from another Snowflake account, as it does not allow uploading, modifying, or unloading data. The company would need to use external tools or interfaces to access the data from the reader account and load it into their own account, which can be slow and expensive. Reference: The answer can be verified from Snowflake’s official documentation on secure data sharing, data lake export, and reader accounts available on their website. Here are some relevant links:
Introduction to Secure Data Sharing | Snowflake Documentation
Data Lake Export Public Preview Is Now Available on Snowflake | Snowflake Blog Managing Reader Accounts | Snowflake Documentation

QUESTION 91
Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported.
What could be causing this?

There were JSON nulls in the recent data imports.

The order of the keys in the JSON was changed.

The recent data imports contained fewer fields than usual.

There were variations in string lengths for the JSON values in the recent data imports.

Data is being imported and stored as JSON in a VARIANT column. Query performance was fine, but most recently, poor query performance has been reported. This could be caused by the following factors:
The order of the keys in the JSON was changed. Snowflake stores semi-structured data internally in a column-like structure for the most common elements, and the remainder in a leftovers-like column. The order of the keys in the JSON affects how Snowflake determines the common elements and how it optimizes the query performance. If the order of the keys in the JSON was changed, Snowflake might have to re-parse the data and re-organize the internal storage, which could result in slower query performance.
There were variations in string lengths for the JSON values in the recent data imports. Non-native values, such as dates and timestamps, are stored as strings when loaded into a VARIANT column. Operations on these values could be slower and also consume more space than when stored in a relational column with the corresponding data type. If there were variations in string lengths for the JSON values in the recent data imports, Snowflake might have to allocate more space and perform more conversions, which could also result in slower query performance.
The other options are not valid causes for poor query performance:
There were JSON nulls in the recent data imports. Snowflake supports two types of null values in semi-structured data: SQL NULL and JSON null. SQL NULL means the value is missing or unknown, while JSON null means the value is explicitly set to null. Snowflake can distinguish between these two types of null values and handle them accordingly. Having JSON nulls in the recent data imports should not affect the query performance significantly.
The recent data imports contained fewer fields than usual. Snowflake can handle semi-structured data with varying schemas and fields. Having fewer fields than usual in the recent data imports should not affect the query performance significantly, as Snowflake can still optimize the data ingestion and query execution based on the existing fields.
Reference:
Considerations for Semi-structured Data Stored in VARIANT
Snowflake Architect Training
Snowflake query performance on unique element in variant column
Snowflake variant performance

QUESTION 92
An Architect is troubleshooting a query with poor performance using the QUERY_HIST0RY function. The Architect observes that the COMPILATIONJHME is greater than the EXECUTIONJTIME.
What is the reason for this?

The query is processing a very large dataset.

The query has overly complex logic.

The query is queued for execution.

The query is reading from remote storage.

QUESTION 93
Which of the below commands will use warehouse credits?

SHOW TABLES LIKE ‘SNOWFL%’;

SELECT MAX(FLAKE_ID) FROM SNOWFLAKE;

SELECT COUNT(*) FROM SNOWFLAKE;

SELECT COUNT(FLAKE_ID) FROM SNOWFLAKE GROUP BY FLAKE_ID;

QUESTION 94
What conditions should be true for a table to consider search optimization

The table size is at least 100 GB

The table is not clustered OR The table is frequently queried on columns other than the primary cluster key

The table can be of any size

QUESTION 95
An Architect executes the following statements in order:
CREATE TABLE emp (id INTEGER);
INSERT INTO emp VALUES (1),(2);
CREATE TEMPORARY TABLE emp (id INTEGER);
INSERT INTO emp VALUES (1);
Then executes:
SELECT COUNT(*) FROM emp;
DROP TABLE emp;
SELECT COUNT(*) FROM emp;
What will be the result?

COUNT() = 2
COUNT() = 1

COUNT() = 1
COUNT() = 2

COUNT() = 2
COUNT() = 2

The final query results in an error.

QUESTION 96
Which feature provides the capability to define an alternate cluster key for a table with an existing cluster key?

External table

Materialized view

Search optimization

Result cache

QUESTION 97
What built-in Snowflake features make use of the change tracking metadata for a table? (Choose two.)

The MERGE command

The UPSERT command

The CHANGES clause

A STREAM object

The CHANGE_DATA_CAPTURE command

QUESTION 98
If a multi-cluster warehouse is resized, the new size applies to

Clusters that are currently running

Clusters that are started after the warehouse is resized

All of the above

QUESTION 99
You have a very large table which is already clustered on columns that are used to retrieve data from the table by a business group. The base table data does not change much. Another business group came to you and requested for a relatively small subset of data from the table which they will query using complex aggregation logic. You know that querying with those columns will take a lot of time because the table is not clustered on those columns.
What is the most optimal solution that you will suggest to the business team?

CREATE A MATERIALIZED VIEW AND CLUSTER THE VIEW ON THOSE COLUMNS

CREATE A SECURE VIEW

CREATE A REGULAR VIEW

The SnowPro Advanced Architect Certification Exam covers a wide range of topics related to Snowflake’s advanced architecture, including advanced Snowflake concepts, Snowflake security and access control, Snowflake data modeling, Snowflake data warehousing, and Snowflake performance tuning. SnowPro Advanced Architect Certification certification exam is designed to test your ability to apply this knowledge to real-world scenarios, and to demonstrate your expertise in designing and implementing Snowflake solutions that meet business needs.

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