Best Preparations of ARA-C01 Exam 2024 SnowPro Advanced Certification Unlimited 135 Questions [Q15-Q38]

Best Preparations of ARA-C01 Exam 2024 SnowPro Advanced Certification Unlimited 135 Questions

Focus on ARA-C01 All-in-One Exam Guide For Quick Preparation.

NEW QUESTION # 15
Which Snowflake objects can be used in a data share? (Select TWO).

• A. Secure view
• B. External table
• C. Stream
• D. Stored procedure
• E. Standard view

Answer: A,B

NEW QUESTION # 16
How do Snowflake databases that are created from shares differ from standard databases that are not created from shares? (Choose three.)

• A. Shared databases are read-only.
• B. Shared databases are not supported by Time Travel.
• C. Shared databases must be refreshed in order for new data to be visible.
• D. Shared databases can also be created as transient databases.
• E. Shared databases will have the PUBLIC or INFORMATION_SCHEMA schemas without explicitly granting these schemas to the share.
• F. Shared databases cannot be cloned.

Answer: A,B,F

Explanation:
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

NEW QUESTION # 17
An Architect needs to automate the daily Import of two files from an external stage into Snowflake. One file has Parquet-formatted data, the other has CSV-formatted data.
How should the data be joined and aggregated to produce a final result set?

• A. Create a task using Snowflake scripting that will import the files, and then call a User-Defined Function (UDF) to produce the final result set.
• B. Create a JavaScript stored procedure to read. join, and aggregate the data directly from the external stage, and then store the results in a table.
• C. Create a materialized view to read, Join, and aggregate the data directly from the external stage, and use the view to produce the final result set
• D. Use Snowpipe to ingest the two files, then create a materialized view to produce the final result set.

Answer: A

Explanation:
According to the Snowflake documentation, tasks are objects that enable scheduling and execution of SQL statements or JavaScript user-defined functions (UDFs) in Snowflake. Tasks can be used to automate data loading, transformation, and maintenance operations. Snowflake scripting is a feature that allows writing procedural logic using SQL statements and JavaScript UDFs. Snowflake scripting can be used to create complex workflows and orchestrate tasks. Therefore, the best option to automate the daily import of two files from an external stage into Snowflake, join and aggregate the data, and produce a final result set is to create a task using Snowflake scripting that will import the files using the COPY INTO command, and then call a UDF to perform the join and aggregation logic. The UDF can return a table or a variant value as the final result set. Reference:
Tasks
Snowflake Scripting
User-Defined Functions

NEW QUESTION # 18
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.)

• A. Avoid sharing data with partner organizations.
• B. Use, at minimum, the Business Critical edition of Snowflake.
• C. Create Dynamic Data Masking policies and apply them to columns that contain PHI.
• D. Rewrite SQL queries to eliminate projections of PHI data based on current_role().
• E. Use the Internal Tokenization feature to obfuscate sensitive data.
• F. Use the External Tokenization feature to obfuscate sensitive data.

Answer: B,C,F

Explanation:
Explanation
* 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

NEW QUESTION # 19
You can define a clustering key directly on top of VARIANT columns

• A. FALSE
• B. TRUE

Answer: A

NEW QUESTION # 20
All multi cluster warehouses that were using the Legacy policy now use the default Standard policy

• A. TRUE
• B. FALSE

Answer: A

NEW QUESTION # 21
Snowflake supports the following query performance optimizing methods

• A. B-tree type indexes
• B. Retrieving results of previous query from cache
• C. Caching techniques

Answer: B,C

NEW QUESTION # 22
How is the change of local time due to daylight savings time handled in Snowflake tasks? (Choose two.)

• A. A frequent task execution schedule like minutes may not cause a problem, but will affect the task history.
• B. Task schedules can be designed to follow specified or local time zones to accommodate the time changes.
• C. A task will move to a suspended state during the daylight savings time change.
• D. A task scheduled in a UTC-based schedule will have no issues with the time changes.
• E. A task schedule will follow only the specified time and will fail to handle lost or duplicated hours.

Answer: A,B

NEW QUESTION # 23
SNOWPIPE_AUTO_INGEST is supported for external stages only

• A. TRUE
• B. FALSE

Answer: A

NEW QUESTION # 24
A company is using a Snowflake account in Azure. The account has SAML SSO set up using ADFS as a SCIM identity provider. To validate Private Link connectivity, an Architect performed the following steps:
* Confirmed Private Link URLs are working by logging in with a username/password account
* Verified DNS resolution by running nslookups against Private Link URLs
* Validated connectivity using SnowCD
* Disabled public access using a network policy set to use the company's IP address range However, the following error message is received when using SSO to log into the company account:
IP XX.XXX.XX.XX is not allowed to access snowflake. Contact your local security administrator.
What steps should the Architect take to resolve this error and ensure that the account is accessed using only Private Link? (Choose two.)

• A. Add the IP address in the error message to the allowed list in the network policy.
• B. Open a case with Snowflake Support to authorize the Private Link URLs' access to the account.
• C. Generate a new SCIM access token using system$generate_scim_access_token and save it to Azure AD.
• D. Alter the Azure security integration to use the Private Link URLs.
• E. Update the configuration of the Azure AD SSO to use the Private Link URLs.

Answer: A,E

Explanation:
The error message indicates that the IP address in the error message is not allowed to access Snowflake because it is not in the allowed list of the network policy. The network policy is a feature that allows restricting access to Snowflake based on IP addresses or ranges. To resolve this error, the Architect should take the following steps:
Add the IP address in the error message to the allowed list in the network policy. This will allow the IP address to access Snowflake using the Private Link URLs. Alternatively, the Architect can disable the network policy if it is not required for security reasons.
Update the configuration of the Azure AD SSO to use the Private Link URLs. This will ensure that the SSO authentication process uses the Private Link URLs instead of the public URLs. The configuration can be updated by following the steps in the Azure documentation1.
These two steps should resolve the error and ensure that the account is accessed using only Private Link. The other options are not necessary or relevant for this scenario. Altering the Azure security integration to use the Private Link URLs is not required because the security integration is used for SCIM provisioning, not for SSO authentication. Generating a new SCIM access token using system$generate_scim_access_token and saving it to Azure AD is not required because the SCIM access token is used for SCIM provisioning, not for SSO authentication. Opening a case with Snowflake Support to authorize the Private Link URLs' access to the account is not required because the authorization can be done by the account administrator using the SYSTEM$AUTHORIZE_PRIVATELINK function2.

NEW QUESTION # 25
Which columns can be included in an external table schema? (Select THREE).

• A. VALUE
• B. METADATASISUPDATE
• C. METADATAS FILE_ROW_NUMBER
• D. METADATASEXTERNAL TABLE PARTITION
• E. METADAT A$ FILENAME
• F. METADATASROW_ID

Answer: B,E,F

NEW QUESTION # 26
What is the recommended strategy to choose the right sized warehouse to achieve best performance based on query processing?

• A. Run homogenous queries on the same warehouse
• B. Run heterogenous queries on the same warehouse

Answer: A

NEW QUESTION # 27
How do Snowflake databases that are created from shares differ from standard databases that are not created from shares? (Choose three.)

• A. Shared databases are read-only.
• B. Shared databases are not supported by Time Travel.
• C. Shared databases must be refreshed in order for new data to be visible.
• D. Shared databases can also be created as transient databases.
• E. Shared databases will have the PUBLIC or INFORMATION_SCHEMA schemas without explicitly granting these schemas to the share.
• F. Shared databases cannot be cloned.

Answer: A,B,F

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 incorrect because 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. Reference: Introduction to Secure Data Sharing | Snowflake Documentation, Cloning Objects | Snowflake Documentation, Time Travel | Snowflake Documentation, Working with Shares | Snowflake Documentation, CREATE DATABASE | Snowflake Documentation

NEW QUESTION # 28
A company's daily Snowflake workload consists of a huge number of concurrent queries triggered between 9pm and 11pm. At the individual level, these queries are smaller statements that get completed within a short time period.
What configuration can the company's Architect implement to enhance the performance of this workload? (Choose two.)

• A. Enable a multi-clustered virtual warehouse in maximized mode during the workload duration.
• B. Set the connection timeout to a higher value than its default.
• C. Set the MAX_CONCURRENCY_LEVEL to a higher value than its default value of 8 at the virtual warehouse level.
• D. Reduce the amount of data that is being processed through this workload.
• E. Increase the size of the virtual warehouse to size X-Large.

Answer: A,E

NEW QUESTION # 29
At which object type level can the APPLY MASKING POLICY, APPLY ROW ACCESS POLICY and APPLY SESSION POLICY privileges be granted?

• A. Table
• B. Schema
• C. Global
• D. Database

Answer: C

Explanation:
The object type level at which the APPLY MASKING POLICY, APPLY ROW ACCESS POLICY and APPLY SESSION POLICY privileges can be granted is global. These are account-level privileges that control who can apply or unset these policies on objects such as columns, tables, views, accounts, or users. These privileges are granted to the ACCOUNTADMIN role by default, and can be granted to other roles as needed. The other options are incorrect because they are not the object type level at which these privileges can be granted. Database, schema, and table are lower-level object types that do not support these privileges. Reference: Access Control Privileges | Snowflake Documentation, Using Dynamic Data Masking | Snowflake Documentation, Using Row Access Policies | Snowflake Documentation, Using Session Policies | Snowflake Documentation

NEW QUESTION # 30
A company is using Snowflake in Azure in the Netherlands. The company analyst team also has data in JSON format that is stored in an Amazon S3 bucket in the AWS Singapore region that the team wants to analyze.
The Architect has been given the following requirements:
1. Provide access to frequently changing data
2. Keep egress costs to a minimum
3. Maintain low latency
How can these requirements be met with the LEAST amount of operational overhead?

• A. Use AWS Transfer Family to replicate data between the S3 bucket in AWS Singapore and an Azure Netherlands Blob storage, then use an external table against the Blob storage.
• B. Use a materialized view on top of an external table against the S3 bucket in AWS Singapore.
• C. Copy the data between providers from S3 to Azure Blob storage to collocate, then use Snowpipe for data ingestion.
• D. Use an external table against the S3 bucket in AWS Singapore and copy the data into transient tables.

Answer: D

Explanation:
: Option A is the best design to meet the requirements because it uses a materialized view on top of an external table against the S3 bucket in AWS Singapore. A materialized view is a database object that contains the results of a query and can be refreshed periodically to reflect changes in the underlying data1. An external table is a table that references data files stored in a cloud storage service, such as Amazon S32. By using a materialized view on top of an external table, the company can provide access to frequently changing data, keep egress costs to a minimum, and maintain low latency. This is because the materialized view will cache the query results in Snowflake, reducing the need to access the external data files and incur network charges. The materialized view will also improve the query performance by avoiding scanning the external data files every time. The materialized view can be refreshed on a schedule or on demand to capture the changes in the external data files1.
Option B is not the best design because it uses an external table against the S3 bucket in AWS Singapore and copies the data into transient tables. A transient table is a table that is not subject to the Time Travel and Fail-safe features of Snowflake, and is automatically purged after a period of time3. By using an external table and copying the data into transient tables, the company will incur more egress costs and operational overhead than using a materialized view. This is because the external table will access the external data files every time a query is executed, and the copy operation will also transfer data from S3 to Snowflake. The transient tables will also consume more storage space in Snowflake and require manual maintenance to ensure they are up to date.
Option C is not the best design because it copies the data between providers from S3 to Azure Blob storage to collocate, then uses Snowpipe for data ingestion. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables4. By copying the data between providers, the company will incur high egress costs and latency, as well as operational complexity and maintenance of the infrastructure. Snowpipe will also add another layer of processing and storage in Snowflake, which may not be necessary if the external data files are already in a queryable format.
Option D is not the best design because it uses AWS Transfer Family to replicate data between the S3 bucket in AWS Singapore and an Azure Netherlands Blob storage, then uses an external table against the Blob storage. AWS Transfer Family is a service that enables secure and seamless transfer of files over SFTP, FTPS, and FTP to and from Amazon S3 or Amazon EFS5. By using AWS Transfer Family, the company will incur high egress costs and latency, as well as operational complexity and maintenance of the infrastructure. The external table will also access the external data files every time a query is executed, which may affect the query performance.

NEW QUESTION # 31
Which statements describe characteristics of the use of materialized views in Snowflake? (Choose two.)

• A. They can include ORDER BY clauses.
• B. They can support MIN and MAX aggregates.
• C. They can support inner joins, but not outer joins.
• D. They cannot include nested subqueries.
• E. They can include context functions, such as CURRENT_TIME().

Answer: B,E

NEW QUESTION # 32
An Architect is designing a pipeline to stream event data into Snowflake using the Snowflake Kafka connector. The Architect's highest priority is to configure the connector to stream data in the MOST cost-effective manner.
Which of the following is recommended for optimizing the cost associated with the Snowflake Kafka connector?

• A. Utilize a higher Buffer.flush.time in the connector configuration.
• B. Utilize a lower Buffer.count.records in the connector configuration.
• C. Utilize a lower Buffer.size.bytes in the connector configuration.
• D. Utilize a higher Buffer.size.bytes in the connector configuration.

Answer: B

NEW QUESTION # 33
Which of the following are characteristics of Snowflake's parameter hierarchy?

• A. Table parameters override virtual warehouse parameters.
• B. Schema parameters override account parameters.
• C. Virtual warehouse parameters override user parameters.
• D. Session parameters override virtual warehouse parameters.

Answer: B

Explanation:
Explanation
This is the correct answer because it reflects the characteristics of Snowflake's parameter hierarchy.
Snowflake provides three types of parameters that can be set for an account: account parameters, session parameters, and object parameters. All parameters have default values, which can be set and then overridden at different levels depending on the parameter type. The following diagram illustrates the hierarchical relationship between the different parameter types and how individual parameters can be overridden at each level1:
As shown in the diagram, schema parameters are a type of object parameters that can be set for schemas.
Schema parameters can override the account parameters that are set at the account level. For example, the LOG_LEVEL parameter can be set at the account level to control the logging level for all objects in the account, but it can also be overridden at the schema level to control the logging level for specific stored procedures and UDFs in that schema2.
The other options listed are not correct because they do not reflect the characteristics of Snowflake's parameter hierarchy. Session parameters do not override virtual warehouse parameters, because virtual warehouse parameters are a type of session parameters that can be set for virtual warehouses. Virtual warehouse parameters do not override user parameters, because user parameters are a type of session parameters that can be set for users. Table parameters do not override virtual warehouse parameters, because table parameters are a type of object parameters that can be set for tables, and object parameters do not affect session parameters1.
References:
* Snowflake Documentation: Parameters
* Snowflake Documentation: Setting Log Level

NEW QUESTION # 34
You have set time-travel retention to 10 days. You now increase the retention period by 10 more days to make it 20 days.
What will be the impact on the table data?

• A. Any data which has not reached the 10 days time-travel period, will now have time-travel extended for 20 days
• B. Changes will impact only new data
• C. Any data that is 10 days older and moved to fail-safe will not have any impact

Answer: A,C

NEW QUESTION # 35
To increase performance, materialized views can be created on external table without any additional cost

• A. FALSE
• B. TRUE

Answer: A

NEW QUESTION # 36
At which object type level can the APPLY MASKING POLICY, APPLY ROW ACCESS POLICY and APPLY SESSION POLICY privileges be granted?

• A. Table
• B. Schema
• C. Database
• D. Global

Answer: A

NEW QUESTION # 37
A media company needs a data pipeline that will ingest customer review data into a Snowflake table, and apply some transformations. The company also needs to use Amazon Comprehend to do sentiment analysis and make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions.
The data pipeline needs to run continuously and efficiently as new records arrive in the object storage leveraging event notifications. Also, the operational complexity, maintenance of the infrastructure, including platform upgrades and security, and the development effort should be minimal.
Which design will meet these requirements?

• A. Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Create an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.
• B. Ingest the data using copy into and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.
• C. Ingest the data using Snowpipe and use streams and tasks to orchestrate transformations. Export the data into Amazon S3 to do model inference with Amazon Comprehend and ingest the data back into a Snowflake table. Then create a listing in the Snowflake Marketplace to make the data available to other companies.
• D. Ingest the data into Snowflake using Amazon EMR and PySpark using the Snowflake Spark connector. Apply transformations using another Spark job. Develop a python program to do model inference by leveraging the Amazon Comprehend text analysis API. Then write the results to a Snowflake table and create a listing in the Snowflake Marketplace to make the data available to other companies.

Answer: A

Explanation:
Option B is the best design to meet the requirements because it uses Snowpipe to ingest the data continuously and efficiently as new records arrive in the object storage, leveraging event notifications. Snowpipe is a service that automates the loading of data from external sources into Snowflake tables1. It also uses streams and tasks to orchestrate transformations on the ingested data. Streams are objects that store the change history of a table, and tasks are objects that execute SQL statements on a schedule or when triggered by another task2. Option B also uses an external function to do model inference with Amazon Comprehend and write the final records to a Snowflake table. An external function is a user-defined function that calls an external API, such as Amazon Comprehend, to perform computations that are not natively supported by Snowflake3. Finally, option B uses the Snowflake Marketplace to make the de-identified final data set available publicly for advertising companies who use different cloud providers in different regions. The Snowflake Marketplace is a platform that enables data providers to list and share their data sets with data consumers, regardless of the cloud platform or region they use4.
Option A is not the best design because it uses copy into to ingest the data, which is not as efficient and continuous as Snowpipe. Copy into is a SQL command that loads data from files into a table in a single transaction. It also exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.
Option C is not the best design because it uses Amazon EMR and PySpark to ingest and transform the data, which also increases the operational complexity and maintenance of the infrastructure. Amazon EMR is a cloud service that provides a managed Hadoop framework to process and analyze large-scale data sets. PySpark is a Python API for Spark, a distributed computing framework that can run on Hadoop. Option C also develops a python program to do model inference by leveraging the Amazon Comprehend text analysis API, which increases the development effort.
Option D is not the best design because it is identical to option A, except for the ingestion method. It still exports the data into Amazon S3 to do model inference with Amazon Comprehend, which adds an extra step and increases the operational complexity and maintenance of the infrastructure.

NEW QUESTION # 38
......

To earn the Snowflake ARA-C01 certification, candidates must pass a rigorous exam that covers a wide range of topics related to Snowflake architecture and functionality. ARA-C01 exam consists of 60 multiple-choice questions and is timed for 90 minutes. ARA-C01 exam is computer-based and can be taken at any authorized testing center or remotely from a candidate's home or office.

 

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