Snowflake ARA-C01 Exam Dumps

Zero-copy cloning is a feature that allows creating a clone of a table, schema, or database without physically copying the data. Zero-copy cloning is suitable for scenarios where the cloned object needs to have the same data and metadata as the original object, and where the cloned object does not need to be modified or updated frequently.Zero-copy cloning is also suitable for scenarios where the cloned object needs to be shared within the same Snowflake account or across different accounts in the same cloud region2

However, zero-copy cloning is not suitable for scenarios where the cloned object needs to have different data or metadata than the original object, or where the cloned object needs to be modified or updated frequently. Zero-copy cloning is also not suitable for scenarios where the cloned object needs to be shared across different accounts in different cloud regions.In these scenarios, copying of data would be required, either by using the COPY INTO command or by using data sharing with secure views3

The following are examples of development and testing scenarios where copying of data would be required, and zero-copy cloning would not be suitable:

Developers create their own datasets to work against transformed versions of the live data. This scenario requires copying of data because the developers need to modify the data or metadata of the cloned object to perform transformations, such as adding, deleting, or updating columns, rows, or values.Zero-copy cloning would not be suitable because it would create a read-only clone that shares the same data and metadata as the original object, and any changes made to the clone would affect the original object as well4

Data is in a production Snowflake account that needs to be provided to Developers in a separate development/testing Snowflake account in the same cloud region. This scenario requires copying of data because the data needs to be shared across different accounts in the same cloud region. Zero-copy cloning would not be suitable because it would create a clone within the same account as the original object, and it would not allow sharing the clone with another account.To share data across different accounts in the same cloud region, data sharing with secure views or COPY INTO command can be used5

The following are examples of development and testing scenarios where zero-copy cloning would be suitable, and copying of data would not be required:

Production and development run in different databases in the same account, and Developers need to see production-like data but with specific columns masked. This scenario can use zero-copy cloning because the data needs to be shared within the same account, and the cloned object does not need to have different data or metadata than the original object. Zero-copy cloning can create a clone of the production database in the development database, and the clone can have the same data and metadata as the original database.To mask specific columns, secure views can be created on top of the clone, and the developers can access the secure views instead of the clone directly6

Developers create their own copies of a standard test database previously created for them in the development account, for their initial development and unit testing. This scenario can use zero-copy cloning because the data needs to be shared within the same account, and the cloned object does not need to have different data or metadata than the original object. Zero-copy cloning can create a clone of the standard test database for each developer, and the clone can have the same data and metadata as the original database.The developers can use the clone for their initial development and unit testing, and any changes made to the clone would not affect the original database or other clones7

The release process requires pre-production testing of changes with data of production scale and complexity. For security reasons, pre-production also runs in the production account. This scenario can use zero-copy cloning because the data needs to be shared within the same account, and the cloned object does not need to have different data or metadata than the original object. Zero-copy cloning can create a clone of the production database in the pre-production database, and the clone can have the same data and metadata as the original database.The pre-production testing can use the clone to test the changes with data of production scale and complexity, and any changes made to the clone would not affect the original database or the production environment8Reference:

1: SnowPro Advanced: Architect | Study Guide9

2: Snowflake Documentation | Cloning Overview

3: Snowflake Documentation | Loading Data Using COPY into a Table

4: Snowflake Documentation | Transforming Data During a Load

5: Snowflake Documentation | Data Sharing Overview

6: Snowflake Documentation | Secure Views

7: Snowflake Documentation | Cloning Databases, Schemas, and Tables

8: Snowflake Documentation | Cloning for Testing and Development

:SnowPro Advanced: Architect | Study Guide

:Cloning Overview

:Loading Data Using COPY into a Table

:Transforming Data During a Load

:Data Sharing Overview

:Secure Views

:Cloning Databases, Schemas, and Tables

:Cloning for Testing and Development

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>- 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>- 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.Reference:

[Snowflake Editions]1

[Replication and Failover/Failback]2

[Client Redirect]3

[Snowflake Account Identifiers]4

A healthcare company that handles PHI data must ensure compliance with relevant privacy standards, such as HIPAA, HITRUST, and GDPR.Snowflake provides several features and best practices to help customers meet their data protection and compliance requirements1.

One best practice recommendation is to use, at minimum, the Business Critical edition of Snowflake.This edition provides the highest level of data protection and security, including end-to-end encryption with customer-managed keys, enhanced object-level security, and HIPAA and HITRUST compliance2. Therefore, option A is correct.

Another best practice recommendation is to create Dynamic Data Masking policies and apply them to columns that contain PHI. Dynamic Data Masking is a feature that allows masking or redacting sensitive data based on the current user's role.This way, only authorized users can view the unmasked data, while others will see masked values, such as NULL, asterisks, or random characters3. Therefore, option B is correct.

A third best practice recommendation is to use the External Tokenization feature to obfuscate sensitive data. External Tokenization is a feature that allows replacing sensitive data with tokens that are generated and stored by an external service, such as Protegrity.This way, the original data is never stored or processed by Snowflake, and only authorized users can access the tokenized data through the external service4. Therefore, option D is correct.

Option C is incorrect, because the Internal Tokenization feature is not available in Snowflake.Snowflake does not provide any native tokenization functionality, but only supports integration with external tokenization services4.

Option E is incorrect, because rewriting SQL queries to eliminate projections of PHI data based on current_role() is not a best practice. This approach is error-prone, inefficient, and hard to maintain.A better alternative is to use Dynamic Data Masking policies, which can automatically mask data based on the user's role without modifying the queries3.

Option F is incorrect, because avoiding sharing data with partner organizations is not a best practice. Snowflake enables secure and governed data sharing with internal and external consumers, such as business units, customers, or partners. Data sharing does not involve copying or moving data, but only granting access privileges to the shared objects.Data sharing can also leverage Dynamic Data Masking and External Tokenization features to protect sensitive data5.

The Snowflake SQL REST API is a REST API that you can use to access and update data in a Snowflake database. You can use this API to execute standard queries and most DDL and DML statements. This API can be used to develop custom applications and integrations that can read and write data to Snowflake without installing any additional software on the application server. Option A is not correct because SnowSQL is a command-line client that requires installation and configuration on the application server. Option B is not correct because the Snowpipe REST API is used to load data from cloud storage into Snowflake tables, not to read or write data to Snowflake. Option D is not correct because the Snowflake ODBC driver is a software component that enables applications to connect to Snowflake using the ODBC protocol, which also requires installation and configuration on the application server.Reference: The answer can be verified from Snowflake's official documentation on the Snowflake SQL REST API available on their website. Here are some relevant links:

Snowflake SQL REST API | Snowflake Documentation

Introduction to the SQL API | Snowflake Documentation

Submitting a Request to Execute SQL Statements | Snowflake Documentation

According to the Snowflake documentation, the following are some considerations for choosing clustering for a table1:

Clustering is optimal when either:

You require the fastest possible response times, regardless of cost.

Your improved query performance offsets the credits required to cluster and maintain the table.

Clustering is most effective when the clustering key is used in the following types of query predicates:

Filter predicates (e.g. WHERE clauses)

Join predicates (e.g. ON clauses)

Grouping predicates (e.g. GROUP BY clauses)

Sorting predicates (e.g. ORDER BY clauses)

Clustering is less effective when the clustering key is not used in any of the above query predicates, or when the clustering key is used in a predicate that requires a function or expression to be applied to the key (e.g. DATE_TRUNC, TO_CHAR, etc.).

For most tables, Snowflake recommends a maximum of 3 or 4 columns (or expressions) per key. Adding more than 3-4 columns tends to increase costs more than benefits.

Based on these considerations, the best option for the clustering key columns is C. C1, C3, C2, because:

These columns are heavily used in filter and join conditions of SELECT queries, which are the most effective types of predicates for clustering.

These columns have high cardinality, which means they have many distinct values and can help reduce the clustering skew and improve the compression ratio.

These columns are likely to be correlated with each other, which means they can help co-locate similar rows in the same micro-partitions and improve the scan efficiency.

These columns do not require any functions or expressions to be applied to them, which means they can be directly used in the predicates without affecting the clustering.