These two actions are possible with an inbound share, according to the Snowflake documentation

and the web search results. An inbound share is a share that is created by another Snowflake account

(the provider) and imported into your account (the consumer). An inbound share allows you to

access the data shared by the provider, but not to modify or delete it. However, you can perform

some actions with the inbound share, such as:

Clone a table from a share. You can create a copy of a table from an inbound share using the CREATE

TABLE … CLONE statement. The clone will contain the same data and metadata as the original table,

but it will be independent of the share. You can modify or delete the clone as you wish, but it will not

reflect any changes made to the original table by the provider1.

Create additional views inside the shared database. You can create views on the tables or views from

an inbound share using the CREATE VIEW statement. The views will be stored in the shared database,

but they will be owned by your account. You can query the views as you would query any other view

in your account, but you cannot modify or delete the underlying objects from the share2.

The other actions listed are not possible with an inbound share, because they would require

modifying the share or the shared objects, which are read-only for the consumer. You cannot grant

modify permissions on the share, create a table from the shared database, or create a table stream

on the shared table34.

Reference:

Cloning Objects from a Share | Snowflake Documentation

Creating Views on Shared Data | Snowflake Documentation

Importing Data from a Share | Snowflake Documentation

Streams on Shared Tables | Snowflake Documentation

In a scenario where strong legal isolation is required alongside the need for multi-tenancy, the most

effective approach is to create separate accounts for each tenant within the Snowflake organization.

This approach ensures complete isolation of data, resources, and management, adhering to strict

legal and compliance requirements. Role-Based Access Control (RBAC) further enhances security by

allowing granular control over who can access what resources within each account. This solution

leverages Snowflake’s capabilities for managing multiple accounts under a single organization

umbrella, ensuring that each tenant's data and operations are isolated from others.

Reference: Snowflake documentation on multi-tenancy and account management, part of the

SnowPro Advanced: Architect learning path.

The Account Per Tenant strategy involves creating separate Snowflake accounts for each tenant

within the multi-tenant application. This approach offers a number of advantages.

Option B: With separate accounts, each tenant's environment is isolated, making security and RBAC

policies simpler to configure and maintain. This is because each account can have its own set of roles

and privileges without the risk of cross-tenant access or the complexity of maintaining a highly

granular permission model within a shared environment.

Option D: This approach also allows for each tenant to have a unique data shape, meaning that the

database schema can be tailored to the specific needs of each tenant without affecting others. This

can be essential when tenants have different data models, usage patterns, or application

customizations.

According to the Snowflake documentation, when loading data into a table that captures the load

time in a column with a default value of either CURRENT_TIME () or CURRENT_TIMESTAMP(), the

default value is evaluated once per COPY statement, not once per row. Therefore, all rows loaded

using a specific COPY statement will have the same timestamp value. This behavior ensures that the

timestamp value reflects the time when the data was loaded into the table, not when the data was

read from the source or created in the source. Reference:

Snowflake Documentation: Loading Data into Tables with Default Values

Snowflake Documentation: COPY INTO table

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

In Snowflake's parameter hierarchy, virtual warehouse parameters take precedence over user

parameters. This hierarchy is designed to ensure that settings at the virtual warehouse level, which

typically reflect the requirements of a specific workload or set of queries, override the p

set at the individual user level. This helps maintain consistent performance and resource utilization

as specified by the administrators managing the virtual warehouses.

Reference: Snowflake documentation on parameter hierarchy, found in the SnowPro Advanced:

Architect learning materials.

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.

Reference:

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

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.

Reference:

Introduction to Replication and Failover

Replicating Databases Across Multiple Accounts

Cloning Considerations

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.

Reference:

Snowflake Documentation: Scaling Policy for Multi-cluster Warehouses

Snowflake Documentation: MAX_CONCURRENCY_LEVEL

The correct answer is B and E because they use the correct syntax and values for the identifier

function and the session variables.

The identifier function allows you to use a variable or expression as an identifier (such as a table

name or column name) in a SQL statement. It takes a single argument and returns it as an identifier.

For example, identifier($tbl_ref) returns EMP_TBL as an identifier.

The session variables are set using the SET command and can be referenced using the $ sign. For

example, $var1 returns Name1 as a value.

Option A is incorrect because it uses Stbl_ref and Scol_ref, which are not valid session variables or

identifiers. They should be $tbl_ref and $col_ref instead.

Option C is incorrect because it uses identifier, which is not a valid syntax for the identifier

function. It should be identifier($tbl_ref) instead.

Option D is incorrect because it uses Cvarl, var2, and var3, which are not valid session variables or

values. They should be $var1, $var2, and $var3 instead. Reference:

Snowflake Documentation: Identifier Function

Snowflake Documentation: Session Variables

Snowflake Learning: SnowPro Advanced: Architect Exam Study Guide