D. The Bl tool needs a data model that allows users to summarize facts across different dimensions, or to drill down from the summaries.
E. The Architect wants to present a simple flattened single view of the data to a particular group of end users.

Answer DescriptionExplanation:
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.

A. Developers create their own datasets to work against transformed versions of the live data.
C. 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.

Answer DescriptionExplanation: 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 region.

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 views.

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 well.

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 used.

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 directly.

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 clones.

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 environment.

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

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

A. The MERGE command
D. A STREAM object

Answer DescriptionExplanation: In Snowflake, the change tracking metadata for a table is utilized by the MERGE command and the STREAM object. The MERGE command uses change tracking to determine how to apply updates and inserts efficiently based on differences between source and target tables. STREAM objects, on the other hand, specifically capture and store change data, enabling incremental processing based on changes made to a table since the last stream offset was committed.

B. They cannot include nested subqueries.
D. They can support MIN and MAX aggregates.

Answer DescriptionExplanation: According to the Snowflake documentation, materialized views have some limitations on the query specification that defines them. One of these limitations is that they cannot include nested subqueries, such as subqueries in the FROM clause or scalar subqueries in the SELECT list. Another limitation is that they cannot include ORDER BY clauses, context functions (such as CURRENT_TIME()), or outer joins. However, materialized views can support MIN and MAX aggregates, as well as other aggregate functions, such as SUM, COUNT, and AVG.

D. Contact Snowflake Support and request that the account's edition be changed.

Answer DescriptionExplanation: To change the edition of a Snowflake account, an organization administrator (ORGADMIN) cannot directly alter the account settings through SQL commands or the Snowflake interface. The proper procedure is to contact Snowflake Support to request an edition change for the account. This ensures that the change is managed correctly and aligns with Snowflake’s operational protocols.

A. Enable a multi-clustered virtual warehouse in maximized mode during the workload duration
B. Set the MAX_CONCURRENCY_LEVEL to a higher value than its default value of 8 at the virtual warehouse level.

Answer DescriptionExplanation:
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.

A. select * from DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS Where substring(CA_ADDRESS_ID,1,8)= substring('AAAAAAAAPHPPLBAAASKDJHASLKDJHASKJD',1,8);
B. select * from DEMO_DB.DEMO_SCH.CUSTOMER_ADDRESS Where CA_ADDRESS_ID= substring('AAAAAAAAPHPPLBAAASKDJHASLKDJHASKJD',1,16);

Answer DescriptionExplanation:
The use of the search optimization service in Snowflake is particularly effective when queries involve operations that match exact substrings or start from the beginning of a string. The ALTER TABLE command adding search optimization specifically for substrings on the CA_ADDRESS_ID field allows the service to create an optimized search path for queries using substring matches.
Option A benefits because it directly matches a substring from the start of the CA_ADDRESS_ID, aligning with the optimization's capability to quickly locate records based on the beginning segments of strings.
Option B also benefits, despite performing a full equality check, because it essentially compares the full length of CA_ADDRESS_ID to a substring, which can leverage the substring index for efficient retrieval.
Options C, D, and E involve patterns that do not start from the beginning of the string or use negations, which are not optimized by the search optimization service configured for starting substring matches.