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

A.
The Connector only works in Snowflake regions that use AWS infrastructure.
A.
The Connector only works in Snowflake regions that use AWS infrastructure.
Answers
B.
The Connector works with all file formats, including text, JSON, Avro, Ore, Parquet, and XML.
B.
The Connector works with all file formats, including text, JSON, Avro, Ore, Parquet, and XML.
Answers
C.
The Connector creates and manages its own stage, file format, and pipe objects.
C.
The Connector creates and manages its own stage, file format, and pipe objects.
Answers
D.
Loads using the Connector will have lower latency than Snowpipe and will ingest data in real time.
D.
Loads using the Connector will have lower latency than Snowpipe and will ingest data in real time.
Answers
Suggested answer: C

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.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

A healthcare company wants to share data with a medical institute. The institute is running a Standard edition of Snowflake; the healthcare company is running a Business Critical edition.

How can this data be shared?

A.
The healthcare company will need to change the institute's Snowflake edition in the accounts panel.
A.
The healthcare company will need to change the institute's Snowflake edition in the accounts panel.
Answers
B.
By default, sharing is supported from a Business Critical Snowflake edition to a Standard edition.
B.
By default, sharing is supported from a Business Critical Snowflake edition to a Standard edition.
Answers
C.
Contact Snowflake and they will execute the share request for the healthcare company.
C.
Contact Snowflake and they will execute the share request for the healthcare company.
Answers
D.
Set the share_restriction parameter on the shared object to false.
D.
Set the share_restriction parameter on the shared object to false.
Answers
Suggested answer: D

Explanation:

By default, Snowflake does not allow sharing data from a Business Critical edition to a non-Business Critical edition. This is because Business Critical edition provides enhanced security and data protection features that are not available in lower editions. However, this restriction can be overridden by setting the share_restriction parameter on the shared object (database, schema, or table) to false. This parameter allows the data provider to explicitly allow sharing data with lower edition accounts. Note that this parameter can only be set by the data provider, not the data consumer. Also, setting this parameter to false may reduce the level of security and data protection for the shared data.

Enable Data Share:Business Critical Account to Lower Edition

Sharing Is Not Allowed From An Account on BUSINESS CRITICAL Edition to an Account On A Lower Edition

SQL Execution Error: Sharing is Not Allowed from an Account on BUSINESS CRITICAL Edition to an Account on a Lower Edition

Snowflake Editions | Snowflake Documentation

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

Explanation:

The minimum value supported for the buffer.flush.time property is 1 (in seconds). For higher average data flow rates, we suggest that you decrease the default value for improved latency. If cost is a greater concern than latency, you could increase the buffer flush time. Be careful to flush the Kafka memory buffer before it becomes full to avoid out of memory exceptions. https://docs.snowflake.com/en/user-guide/data-load-snowpipe-streaming-kafka

An Architect has chosen to separate their Snowflake Production and QA environments using two separate Snowflake accounts.

The QA account is intended to run and test changes on data and database objects before pushing those changes to the Production account. It is a requirement that all database objects and data in the QA account need to be an exact copy of the database objects, including privileges and data in the Production account on at least a nightly basis.

Which is the LEAST complex approach to use to populate the QA account with the Production account's data and database objects on a nightly basis?

A.
1) Create a 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
A.
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
Answers
B.
1) Create a stage in the Production account 2) Create a stage in the QA account that points to the same external object-storage location 3) Create a task that runs nightly to unload each table in the Production account into the stage 4) Use Snowpipe to populate the QA account
B.
1) Create a 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
Answers
C.
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
C.
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
Answers
D.
1) In the Production account, create an external function that connects into the QA account and returns all the data for one specific table 2) Run the external function as part of a stored procedure that loops through each table in the Production account and populates each table in the QA account
D.
1) 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
Answers
Suggested answer: C

Explanation:

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

Introduction to Replication and Failover

Replicating Databases Across Multiple Accounts

Cloning Considerations

A user can change object parameters using which of the following roles?

A.
ACCOUNTADMIN, SECURITYADMIN
A.
ACCOUNTADMIN, SECURITYADMIN
Answers
B.
SYSADMIN, SECURITYADMIN
B.
SYSADMIN, SECURITYADMIN
Answers
C.
ACCOUNTADMIN, USER with PRIVILEGE
C.
ACCOUNTADMIN, USER with PRIVILEGE
Answers
D.
SECURITYADMIN, USER with PRIVILEGE
D.
SECURITYADMIN, USER with PRIVILEGE
Answers
Suggested answer: C

Explanation:

According to the Snowflake documentation, object parameters are parameters that can be set on individual objects such as databases, schemas, tables, and stages. Object parameters can be set by users with the appropriate privileges on the objects. For example, to set the object parameter AUTO_REFRESH on a table, the user must have the MODIFY privilege on the table. The ACCOUNTADMIN role has the highest level of privileges on all objects in the account, so it can set any object parameter on any object. However, other roles, such as SECURITYADMIN or SYSADMIN, do not have the same level of privileges on all objects, so they cannot set object parameters on objects they do not own or have the required privileges on. Therefore, the correct answer is C. ACCOUNTADMIN, USER with PRIVILEGE.

Parameters | Snowflake Documentation

Object Parameters | Snowflake Documentation

Object Privileges | Snowflake Documentation

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 ang 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 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.
A.
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.
Answers
B.
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 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.
Answers
C.
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.
C.
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.
Answers
D.
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 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.
Answers
Suggested answer: B

Explanation:

This design meets all the requirements for the data pipeline. Snowpipe is a feature that enables continuous data loading into Snowflake from object storage using event notifications. It is efficient, scalable, and serverless, meaning it does not require any infrastructure or maintenance from the user. Streams and tasks are features that enable automated data pipelines within Snowflake, using change data capture and scheduled execution. They are also efficient, scalable, and serverless, and they simplify the data transformation process. External functions are functions that can invoke external services or APIs from within Snowflake. They can be used to integrate with Amazon Comprehend and perform sentiment analysis on the data. The results can be written back to a Snowflake table using standard SQL commands. Snowflake Marketplace is a platform that allows data providers to share data with data consumers across different accounts, regions, and cloud platforms. It is a secure and easy way to make data publicly available to other companies.

Snowpipe Overview | Snowflake Documentation

Introduction to Data Pipelines | Snowflake Documentation

External Functions Overview | Snowflake Documentation

Snowflake Data Marketplace Overview | Snowflake Documentation

Which Snowflake data modeling approach is designed for BI queries?

A.
3 NF
A.
3 NF
Answers
B.
Star schema
B.
Star schema
Answers
C.
Data Vault
C.
Data Vault
Answers
D.
Snowflake schema
D.
Snowflake schema
Answers
Suggested answer: B

Explanation:

A star schema is a Snowflake data modeling approach that is designed for BI queries. A star schema is a type of dimensional modeling that organizes data into fact tables and dimension tables. A fact table contains the measures or metrics of the business process, such as sales amount, order quantity, or profit margin. A dimension table contains the attributes or descriptors of the business process, such as product name, customer name, or order date. A star schema is called so because it resembles a star, with one fact table in the center and multiple dimension tables radiating from it. A star schema can improve the performance and simplicity of BI queries by reducing the number of joins, providing fast access to aggregated data, and enabling intuitive query syntax.A star schema can also support various types of analysis, such as trend analysis, slice and dice, drill down, and roll up12.

Snowflake Documentation: Dimensional Modeling

Snowflake Documentation: Star Schema

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

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

Explanation:

According to the Snowflake documentation1and the web search results2, these two statements are true about how the change of local time due to daylight savings time is handled in Snowflake tasks. A task is a feature that allows scheduling and executing SQL statements or stored procedures in Snowflake. A task can be scheduled using a cron expression that specifies the frequency and time zone of the task execution.

A task scheduled in a UTC-based schedule will have no issues with the time changes. UTC is a universal time standard that does not observe daylight savings time.Therefore, a task that uses UTC as the time zone will run at the same time throughout the year, regardless of the local time changes1.

Task schedules can be designed to follow specified or local time zones to accommodate the time changes. Snowflake supports using any valid IANA time zone identifier in the cron expression for a task. This allows the task to run according to the local time of the specified time zone, which may include daylight savings time adjustments.For example, a task that uses Europe/London as the time zone will run one hour earlier or later when the local time switches between GMT and BST12.

Snowflake Documentation: Scheduling Tasks

Snowflake Community: Do the timezones used in scheduling tasks in Snowflake adhere to daylight savings?

An Architect needs to grant a group of ORDER_ADMIN users the ability to clean old data in an ORDERS table (deleting all records older than 5 years), without granting any privileges on the table. The group's manager (ORDER_MANAGER) has full DELETE privileges on the table.

How can the ORDER_ADMIN role be enabled to perform this data cleanup, without needing the DELETE privilege held by the ORDER_MANAGER role?

A.
Create a stored procedure that runs with caller's rights, including the appropriate '> 5 years' business logic, and grant USAGE on this procedure to ORDER_ADMIN. The ORDER_MANAGER role owns the procedure.
A.
Create a stored procedure that runs with caller's rights, including the appropriate '> 5 years' business logic, and grant USAGE on this procedure to ORDER_ADMIN. The ORDER_MANAGER role owns the procedure.
Answers
B.
Create a stored procedure that can be run using both caller's and owner's rights (allowing the user to specify which rights are used during execution), and grant USAGE on this procedure to ORDER_ADMIN. The ORDER_MANAGER role owns the procedure.
B.
Create a stored procedure that can be run using both caller's and owner's rights (allowing the user to specify which rights are used during execution), and grant USAGE on this procedure to ORDER_ADMIN. The ORDER_MANAGER role owns the procedure.
Answers
C.
Create a stored procedure that runs with owner's rights, including the appropriate '> 5 years' business logic, and grant USAGE on this procedure to ORDER_ADMIN. The ORDER_MANAGER role owns the procedure.
C.
Create a stored procedure that runs with owner's rights, including the appropriate '> 5 years' business logic, and grant USAGE on this procedure to ORDER_ADMIN. The ORDER_MANAGER role owns the procedure.
Answers
D.
This scenario would actually not be possible in Snowflake -- any user performing a DELETE on a table requires the DELETE privilege to be granted to the role they are using.
D.
This scenario would actually not be possible in Snowflake -- any user performing a DELETE on a table requires the DELETE privilege to be granted to the role they are using.
Answers
Suggested answer: C

Explanation:

This is the correct answer because it allows the ORDER_ADMIN role to perform the data cleanup without needing the DELETE privilege on the ORDERS table. A stored procedure is a feature that allows scheduling and executing SQL statements or stored procedures in Snowflake. A stored procedure can run with either the caller's rights or the owner's rights. A caller's rights stored procedure runs with the privileges of the role that called the stored procedure, while an owner's rights stored procedure runs with the privileges of the role that created the stored procedure. By creating a stored procedure that runs with owner's rights, the ORDER_MANAGER role can delegate the specific task of deleting old data to the ORDER_ADMIN role, without granting the ORDER_ADMIN role more general privileges on the ORDERS table. The stored procedure must include the appropriate business logic to delete only the records older than 5 years, and the ORDER_MANAGER role must grant the USAGE privilege on the stored procedure to the ORDER_ADMIN role.The ORDER_ADMIN role can then execute the stored procedure to perform the data cleanup12.

Snowflake Documentation: Stored Procedures

Snowflake Documentation: Understanding Caller's Rights and Owner's Rights Stored Procedures

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

Explanation:

These two configuration options can enhance the performance of the workload that consists of a huge number of concurrent queries that are smaller and faster.

Enabling a multi-clustered virtual warehouse in maximized mode allows the warehouse to scale out automatically by adding more clusters as soon as the current cluster is fully loaded, regardless of the number of queries in the queue. This can improve the concurrency and throughput of the workload by minimizing or preventing queuing.The maximized mode is suitable for workloads that require high performance and low latency, and are less sensitive to credit consumption1.

Setting the MAX_CONCURRENCY_LEVEL to a higher value than its default value of 8 at the virtual warehouse level allows the warehouse to run more queries concurrently on each cluster. This can improve the utilization and efficiency of the warehouse resources, especially for smaller and faster queries that do not require a lot of processing power.The MAX_CONCURRENCY_LEVEL parameter can be set when creating or modifying a warehouse, and it can be changed at any time2.

Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses

Snowflake Documentation: MAX_CONCURRENCY_LEVEL

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