Snowflake ARA-C01 Real Exam Questions Guaranteed Updated Dump from PrepAwayTest [Q81-Q103]

Q81. What is a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka?

The Connector only works in Snowflake regions that use AWS infrastructure.

The Connector works with all file formats, including text, JSON, Avro, Ore, Parquet, and XML.

The Connector creates and manages its own stage, file format, and pipe objects.

Loads using the Connector will have lower latency than Snowpipe and will ingest data in real time.

Explanation
According to the SnowPro Advanced: Architect documents and learning resources, a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka is that the Connector creates and manages its own stage, file format, and pipe objects. The stage is an internal stage that is used to store the data files from the Kafka topics. The file format is a JSON or Avro file format that is used to parse the data files. The pipe is a Snowpipe object that is used to load the data files into the Snowflake table. The Connector automatically creates and configures these objects based on the Kafka configuration properties, and handles the cleanup and maintenance of these objects1.
The other options are incorrect because they are not characteristics of loading data into Snowflake using the Snowflake Connector for Kafka. Option A is incorrect because the Connector works in Snowflake regions that use any cloud infrastructure, not just AWS. The Connector supports AWS, Azure, and Google Cloud platforms, and can load data across different regions and cloud platforms using data replication2. Option B is incorrect because the Connector does not work with all file formats, only JSON and Avro. The Connector expects the data in the Kafka topics to be in JSON or Avro format, and parses the data accordingly. Other file formats, such as text, ORC, Parquet, or XML, are not supported by the Connector3. Option D is incorrect because loads using the Connector do not have lower latency than Snowpipe, and do not ingest data in real time. The Connector uses Snowpipe to load data into Snowflake, and inherits the same latency and performance characteristics of Snowpipe. The Connector does not provide real-time ingestion, but near real-time ingestion, depending on the frequency and size of the data files4. References: Installing and Configuring the Kafka Connector | Snowflake Documentation, Sharing Data Across Regions and Cloud Platforms | Snowflake Documentation, Overview of the Kafka Connector | Snowflake Documentation, Using Snowflake Connector for Kafka With Snowpipe Streaming | Snowflake Documentation

Q82. COMPRESSION = AUTO can automatically detect below compression techniques when FORMAT TYPE is CSV

GZIP

BZ2

BROTLI

ZSTD

DEFLATE

RAW_DEFLATE

Q83. A Snowflake Architect is designing a multiple-account design strategy.
This strategy will be MOST cost-effective with which scenarios? (Select TWO).

The company wants to clone a production database that resides on AWS to a development database that resides on Azure.

The company needs to share data between two databases, where one must support Payment Card Industry Data Security Standard (PCI DSS) compliance but the other one does not.

The company needs to support different role-based access control features for the development, test, and production environments.

The company security policy mandates the use of different Active Directory instances for the development, test, and production environments.

The company must use a specific network policy for certain users to allow and block given IP addresses.

A multiple-account design strategy is a way of organizing Snowflake accounts into logical groups based on different criteria, such as cloud provider, region, environment, or business unit. A multiple-account design strategy can help achieve various goals, such as cost optimization, performance isolation, security compliance, and data sharing1. In this question, the scenarios that would be most cost-effective with a multiple-account design strategy are:
* The company wants to clone a production database that resides on AWS to a development database that resides on Azure. This scenario would benefit from a multiple-account design strategy because it would allow the company to leverage the cross-cloud replication feature of Snowflake, which enables replicating databases across different cloud platforms and regions. This feature can help reduce the data transfer costs and latency, as well as provide high availability and disaster recovery2.
* The company security policy mandates the use of different Active Directory instances for the development, test, and production environments. This scenario would benefit from a multiple-account design strategy because it would allow the company to use different federated authentication methods for each environment, and integrate them with different Active Directory instances. This can help improve the security and governance of the access to the Snowflake accounts, as well as simplify the user management and provisioning3.
The other scenarios would not be most cost-effective with a multiple-account design strategy, because:
* The company needs to share data between two databases, where one must support Payment Card Industry Data Security Standard (PCI DSS) compliance but the other one does not. This scenario can be handled within a single Snowflake account, by using secure views and secure UDFs to mask or filter the sensitive data, and applying the appropriate roles and privileges to the users who access the data. This can help achieve the PCI DSS compliance without incurring the additional costs of managing multiple accounts4.
* The company needs to support different role-based access control features for the development, test, and production environments. This scenario can also be handled within a single Snowflake account, by using the native role-based access control (RBAC) features of Snowflake, such as roles, grants, and privileges, to define different access levels and permissions for each environment. This can help ensure the security and integrity of the data and the objects, as well as the separation of duties and responsibilities among the users.
* The company must use a specific network policy for certain users to allow and block given IP addresses.
This scenario can also be handled within a single Snowflake account, by using the network policy
* feature of Snowflake, which enables creating and applying network policies to restrict the IP addresses that can access the Snowflake account. This can help prevent unauthorized access and protect the data from malicious attacks.
References:
* Designing Your Snowflake Topology
* Cross-Cloud Replication
* Configuring Federated Authentication and SSO
* Using Secure Views and Secure UDFs to Comply with PCI DSS
* [Understanding Access Control in Snowflake]
* [Network Policies]

Q84. Two queries are run on the customer_address table:
create or replace TABLE CUSTOMER_ADDRESS ( CA_ADDRESS_SK NUMBER(38,0), CA_ADDRESS_ID VARCHAR(16), CA_STREET_NUMBER VARCHAR(IO) CA_STREET_NAME VARCHAR(60), CA_STREET_TYPE VARCHAR(15), CA_SUITE_NUMBER VARCHAR(10), CA_CITY VARCHAR(60), CA_COUNTY VARCHAR(30), CA_STATE VARCHAR(2), CA_ZIP VARCHAR(10), CA_COUNTRY VARCHAR(20), CA_GMT_OFFSET NUMBER(5,2), CA_LOCATION_TYPE VARCHAR(20) ); ALTER TABLE DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS ADD SEARCH OPTIMIZATION ON SUBSTRING(CA_ADDRESS_ID); Which queries will benefit from the use of the search optimization service? (Select TWO).

select * from DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS Where substring(CA_ADDRESS_ID,1,8)= substring(‘AAAAAAAAPHPPLBAAASKDJHASLKDJHASKJD’,1,8);

select * from DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS Where CA_ADDRESS_ID= substring(‘AAAAAAAAPHPPLBAAASKDJHASLKDJHASKJD’,1,16);

select * from DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS Where CA_ADDRESS_ID LIKE ‘%BAAASKD%’;

select * from DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS Where CA_ADDRESS_ID LIKE ‘%PHPP%’;

select * from DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS Where CA_ADDRESS_ID NOT LIKE ‘%AAAAAAAAPHPPL%’;

Q85. Which of the following are characteristics of how row access policies can be applied to external tables?
(Choose three.)

An external table can be created with a row access policy, and the policy can be applied to the VALUE column.

A row access policy can be applied to the VALUE column of an existing external table.

A row access policy cannot be directly added to a virtual column of an external table.

External tables are supported as mapping tables in a row access policy.

While cloning a database, both the row access policy and the external table will be cloned.

A row access policy cannot be applied to a view created on top of an external table.

These three statements are true according to the Snowflake documentation and the web search results. A row access policy is a feature that allows filtering rows based on user-defined conditions. A row access policy can be applied to an external table, which is a table that reads data from external files in a stage. However, there are some limitations and considerations for using row access policies with external tables.
* An external table can be created with a row access policy by using the WITH ROW ACCESS POLICY clause in the CREATE EXTERNAL TABLE statement. The policy can be applied to the VALUE column, which is the column that contains the raw data from the external files in a VARIANT data type1.
* A row access policy can also be applied to the VALUE column of an existing external table by using the ALTER TABLE statement with the SET ROW ACCESS POLICY clause2.
* A row access policy cannot be directly added to a virtual column of an external table. A virtual column is a column that is derived from the VALUE column using an expression. To apply a row access policy to a virtual column, the policy must be applied to the VALUE column and the expression must be repeated in the policy definition3.
* External tables are not supported as mapping tables in a row access policy. A mapping table is a table that is used to determine the access rights of users or roles based on some criteria. Snowflake does not support using an external table as a mapping table because it may cause performance issues or errors4.
* While cloning a database, Snowflake clones the row access policy, but not the external table. Therefore, the policy in the cloned database refers to a table that is not present in the cloned database. To avoid this issue, the external table must be manually cloned or recreated in the cloned database4.
* A row access policy can be applied to a view created on top of an external table. The policy can be applied to the view itself or to the underlying external table. However, if the policy is applied to the view, the view must be a secure view, which is a view that hides the underlying data and the view definition from unauthorized users5.
References:
* CREATE EXTERNAL TABLE | Snowflake Documentation
* ALTER EXTERNAL TABLE | Snowflake Documentation
* Understanding Row Access Policies | Snowflake Documentation
* Snowflake Data Governance: Row Access Policy Overview
* Secure Views | Snowflake Documentation

Q86. The insertReport endpoint can be thought of like the UNIX command tail

TRUE

FALSE

Q87. An Architect entered the following commands in sequence:

USER1 cannot find the table.
Which of the following commands does the Architect need to run for USER1 to find the tables using the Principle of Least Privilege? (Choose two.)

GRANT ROLE PUBLIC TO ROLE INTERN;

GRANT USAGE ON DATABASE SANDBOX TO ROLE INTERN;

GRANT USAGE ON SCHEMA SANDBOX.PUBLIC TO ROLE INTERN;

GRANT OWNERSHIP ON DATABASE SANDBOX TO USER INTERN;

GRANT ALL PRIVILEGES ON DATABASE SANDBOX TO ROLE INTERN;

Q88. A table for IOT devices that measures water usage is created. The table quickly becomes large and contains more than 2 billion rows.

The general query patterns for the table are:
1. DeviceId, lOT_timestamp and Customerld are frequently used in the filter predicate for the select statement
2. The columns City and DeviceManuf acturer are often retrieved
3. There is often a count on Uniqueld
Which field(s) should be used for the clustering key?

lOT_timestamp

City and DeviceManuf acturer

Deviceld and Customerld

Uniqueld

Q89. How do Snowflake databases that are created from shares differ from standard databases that are not created from shares? (Choose three.)

Shared databases are read-only.

Shared databases must be refreshed in order for new data to be visible.

Shared databases cannot be cloned.

Shared databases are not supported by Time Travel.

Shared databases will have the PUBLIC or INFORMATION_SCHEMA schemas without explicitly granting these schemas to the share.

Shared databases can also be created as transient databases.

Explanation
According to the SnowPro Advanced: Architect documents and learning resources, the ways that Snowflake databases that are created from shares differ from standard databases that are not created from shares are:
* Shared databases are read-only. This means that the data consumers who access the shared databases cannot modify or delete the data or the objects in the databases. The data providers who share the databases have full control over the data and the objects, and can grant or revoke privileges on them1.
* Shared databases cannot be cloned. This means that the data consumers who access the shared databases cannot create a copy of the databases or the objects in the databases. The data providers who share the databases can clone the databases or the objects, but the clones are not automatically shared2.
* Shared databases are not supported by Time Travel. This means that the data consumers who access the shared databases cannot use the AS OF clause to query historical data or restore deleted data. The data providers who share the databases can use Time Travel on the databases or the objects, but the historical data is not visible to the data consumers3.
The other options are incorrect because they are not ways that Snowflake databases that are created from shares differ from standard databases that are not created from shares. Option B is incorrectbecause shared databases do not need to be refreshed in order for new data to be visible. The data consumers who access the shared databases can see the latest data as soon as the data providers update the data1. Option E is incorrect because shared databases will not have the PUBLIC or INFORMATION_SCHEMA schemas without explicitly granting these schemas to the share. The data consumers who access the shared databases can only see the objects that the data providers grant to the share, and the PUBLIC and INFORMATION_SCHEMA schemas are not granted by default4. Option F is incorrect because shared databases cannot be created as transient databases. Transient databases are databases that do not support Time Travel or Fail-safe, and can be dropped without affecting the retention period of the data. Shared databases are always created as permanent databases, regardless of the type of the source database5. References: Introduction to Secure Data Sharing | Snowflake Documentation, Cloning Objects | Snowflake Documentation, Time Travel | Snowflake Documentation, Working with Shares | Snowflake Documentation, CREATE DATABASE | Snowflake Documentation

Q90. A company is storing large numbers of small JSON files (ranging from 1-4 bytes) that are received from IoT devices and sent to a cloud provider. In any given hour, 100,000 files are added to the cloud provider.
What is the MOST cost-effective way to bring this data into a Snowflake table?

An external table

A pipe

A stream

A copy command at regular intervals

Explanation
* A pipe is a Snowflake object that continuously loads data from files in a stage (internal or external) into a table. A pipe can be configured to use auto-ingest, which means that Snowflake automatically detects new or modified files in the stage and loads them into the table without any manual intervention1.
* A pipe is the most cost-effective way to bring large numbers of small JSON files into a Snowflake table, because it minimizes the number of COPY commands executed and the number of micro-partitions created. A pipe can use file aggregation, which means that it can combine multiple small files into a single larger file before loading them into the table. This reduces the load time and the storage cost of the data2.
* An external table is a Snowflake object that references data files stored in an external location, such as Amazon S3, Google Cloud Storage, or Microsoft Azure Blob Storage. An external table does not store the data in Snowflake, but only provides a view of the data for querying. An external table is not a cost-effective way to bring data into a Snowflake table, because it does not support file aggregation, and it requires additional network bandwidth and compute resources to query the external data3.
* A stream is a Snowflake object that records the history of changes (inserts, updates, and deletes) made to a table. A stream can be used to consume the changes from a table and apply them to another table or a task. A stream is not a way to bring data into a Snowflake table, but a way to process the data after it is loaded into a table4.
* A copy command is a Snowflake command that loads data from files in a stage into a table. A copy command can be executed manually or scheduled using a task. A copy command is not a cost-effective way to bring large numbers of small JSON files into a Snowflake table, because it does not support file aggregation, and it may create many micro-partitions that increase the storage cost of the data5.
References: : Pipes : Loading Data Using Snowpipe : External Tables : Streams : COPY INTO <table>

Q91. 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).

Snowflake cannot handle the joins implied in a 3NF data model.

The Architect wants to remove data duplication from the data stored in Snowflake.

The Architect is designing a landing zone to receive raw data into Snowflake.

The Bl tool needs a data model that allows users to summarize facts across different dimensions, or to drill down from the summaries.

The Architect wants to present a simple flattened single view of the data to a particular group of end users.

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.
Reference:
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

Q92. Below are the rest APIs provided by Snowpipe

insertFiles

insertReport

loadData

Q93. A healthcare company is deploying a Snowflake account that may include Personal Health Information (PHI).
The company must ensure compliance with all relevant privacy standards.
Which best practice recommendations will meet data protection and compliance requirements? (Choose three.)

Use, at minimum, the Business Critical edition of Snowflake.

Create Dynamic Data Masking policies and apply them to columns that contain PHI.

Use the Internal Tokenization feature to obfuscate sensitive data.

Use the External Tokenization feature to obfuscate sensitive data.

Rewrite SQL queries to eliminate projections of PHI data based on current_role().

Avoid sharing data with partner organizations.

* 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.
References: : Snowflake’s Security & Compliance Reports : Snowflake Editions : Dynamic Data Masking : External Tokenization : Secure Data Sharing

Q94. You are running a large join on snowflake. You ran it on a medium warehouse and it took almost an hour to run. You then tried to run the join on a large warehouse but still the performance did not improve.
What may be the most possible cause of this.

There may be a symptom on skew in your data where one of the value of the column is significantly more than rest of the values in the column

Your warehouses do not have enough memory

Since you have configured an warehouse with a low auto-suspend value, your warehouse is going down frequently

Q95. When activating Tri-Secret Secure in a hierarchical encryption model in a Snowflake account, at what level is the customer-managed key used?

At the root level (HSM)

At the account level (AMK)

At the table level (TMK)

At the micro-partition level

Q96. 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

Q97. Which of the following are characteristics of Snowflake’s parameter hierarchy?

Session parameters override virtual warehouse parameters.

Virtual warehouse parameters override user parameters.

Table parameters override virtual warehouse parameters.

Schema parameters override account parameters.

Q98. What is a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka?

The Connector only works in Snowflake regions that use AWS infrastructure.

The Connector works with all file formats, including text, JSON, Avro, Ore, Parquet, and XML.

The Connector creates and manages its own stage, file format, and pipe objects.

Loads using the Connector will have lower latency than Snowpipe and will ingest data in real time.

According to the SnowPro Advanced: Architect documents and learning resources, a characteristic of loading data into Snowflake using the Snowflake Connector for Kafka is that the Connector creates and manages its own stage, file format, and pipe objects. The stage is an internal stage that is used to store the data files from the Kafka topics. The file format is a JSON or Avro file format that is used to parse the data files. The pipe is a Snowpipe object that is used to load the data files into the Snowflake table. The Connector automatically creates and configures these objects based on the Kafka configuration properties, and handles the cleanup and maintenance of these objects1.
The other options are incorrect because they are not characteristics of loading data into Snowflake using the Snowflake Connector for Kafka. Option A is incorrect because the Connector works in Snowflake regions that use any cloud infrastructure, not just AWS. The Connector supports AWS, Azure, and Google Cloud platforms, and can load data across different regions and cloud platforms using data replication2. Option B is incorrect because the Connector does not work with all file formats, only JSON and Avro. The Connector expects the data in the Kafka topics to be in JSON or Avro format, and parses the data accordingly. Other file formats, such as text, ORC, Parquet, or XML, are not supported by the Connector3. Option D is incorrect because loads using the Connector do not have lower latency than Snowpipe, and do not ingest data in real time. The Connector uses Snowpipe to load data into Snowflake, and inherits the same latency and performance characteristics of Snowpipe. The Connector does not provide real-time ingestion, but near real-time ingestion, depending on the frequency and size of the data files4. Reference: Installing and Configuring the Kafka Connector | Snowflake Documentation, Sharing Data Across Regions and Cloud Platforms | Snowflake Documentation, Overview of the Kafka Connector | Snowflake Documentation, Using Snowflake Connector for Kafka With Snowpipe Streaming | Snowflake Documentation

Q99. 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?

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

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

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

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

Q100. How will you drop a cluster key?

ALTER TABLE <name> DROP CLUSTERING KEY

ALTER TABLE <name> DELETE CLUSTERING KEY

ALTER TABLE <name> REMOVE CLUSTERING KEY

Q101. The following DDL command was used to create a task based on a stream:

Assuming MY_WH is set to auto_suspend – 60 and used exclusively for this task, which statement is true?

The warehouse MY_WH will be made active every five minutes to check the stream.

The warehouse MY_WH will only be active when there are results in the stream.

The warehouse MY_WH will never suspend.

The warehouse MY_WH will automatically resize to accommodate the size of the stream.

Q102. What integration object should be used to place restrictions on where data may be exported?

Stage integration

Security integration

Storage integration

API integration

According to the SnowPro Advanced: Architect documents and learning resources, the integration object that should be used to place restrictions on where data may be exported is the security integration. A security integration is a Snowflake object that provides an interface between Snowflake and third-party security services, such as Okta, Duo, or Google Authenticator. A security integration can be used to enforce policies on data export, such as requiring multi-factor authentication (MFA) or restricting the export destination to a specific network or domain. A security integration can also be used to enable single sign-on (SSO) or federated authentication for Snowflake users1.
The other options are incorrect because they are not integration objects that can be used to place restrictions on where data may be exported. Option A is incorrect because a stage integration is not a valid type of integration object in Snowflake. A stage is a Snowflake object that references a location where data files are stored, such as an internal stage, an external stage, or a named stage. A stage is not an integration object that provides an interface between Snowflake and third-party services2. Option C is incorrect because a storage integration is a Snowflake object that provides an interface between Snowflake and external cloud storage, such as Amazon S3, Azure Blob Storage, or Google Cloud Storage. A storage integration can be used to securely access data files from external cloud storage without exposing the credentials, but it cannot be used to place restrictions on where data may be exported3. Option D is incorrect because an API integration is a Snowflake object that provides an interface between Snowflake and third-party services that use REST APIs, such as Salesforce, Slack, or Twilio. An API integration can be used to securely call external REST APIs from Snowflake using the CALL_EXTERNAL_API function, but it cannot be used to place restrictions on where data may be exported4. References: CREATE SECURITY INTEGRATION | Snowflake Documentation, CREATE STAGE | Snowflake Documentation, CREATE STORAGE INTEGRATION | Snowflake Documentation, CREATE API INTEGRATION | Snowflake Documentation

Q103. Which security, governance, and data protection features require, at a MINIMUM, the Business Critical edition of Snowflake? (Choose two.)

Extended Time Travel (up to 90 days)

Customer-managed encryption keys through Tri-Secret Secure

Periodic rekeying of encrypted data

AWS, Azure, or Google Cloud private connectivity to Snowflake

Federated authentication and SSO

Leave a Reply Cancel reply