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| Section | Weight | Objectives |
|---|---|---|
| Data Engineering | 25% | - Data Transformation
|
| Performance Optimization | 20% | - Query Performance
|
| Snowflake Architecture | 30% | - Platform Architecture Design
|
| Accounts and Security | 25% | - Access Control and Authentication
|
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NEW QUESTION # 159
Consider the following scenario where a masking policy is applied on the CREDICARDND column of the CREDITCARDINFO table. The masking policy definition Is as follows:
Sample data for the CREDITCARDINFO table is as follows:
NAME EXPIRYDATE CREDITCARDNO
JOHN DOE 2022-07-23 4321 5678 9012 1234
if the Snowflake system rotes have not been granted any additional roles, what will be the result?
Answer: D
Explanation:
The masking policy defined in the image indicates that if a user has the PI_ANALYTICS role, they will be able to see the last 4 characters of the CREDITCARDNO column data in clear text. Otherwise, they will see 'MASKED'. Since Snowflake system roles have not been granted any additional roles, they won't have the PI_ANALYTICS role and therefore cannot view the last 4 characters of credit card numbers.
To apply a masking policy on a column in Snowflake, you need to use the ALTER TABLE ... ALTER COLUMN command or the ALTER VIEW command and specify the policy name. For example, to apply the creditcardno_mask policy on the CREDITCARDNO column of the CREDITCARDINFO table, you can use the following command:
ALTER TABLE CREDITCARDINFO ALTER COLUMN CREDITCARDNO SET MASKING POLICY creditcardno_mask; For more information on how to create and use masking policies in Snowflake, you can refer to the following resources:
CREATE MASKING POLICY: This document explains the syntax and usage of the CREATE MASKING POLICY command, which allows you to create a new masking policy or replace an existing one.
Using Dynamic Data Masking: This guide provides instructions on how to configure and use dynamic data masking in Snowflake, which is a feature that allows you to mask sensitive data based on the execution context of the user.
ALTER MASKING POLICY: This document explains the syntax and usage of the ALTER MASKING POLICY command, which allows you to modify the properties of an existing masking policy.
NEW QUESTION # 160
Which of the following are characteristics of Snowflake's parameter hierarchy?
Answer: B
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.
Reference:
Snowflake Documentation: Parameters
Snowflake Documentation: Setting Log Level
NEW QUESTION # 161
Which functions does the Data Build Tool (dbt) facilitate? (Select TWO).
Answer: D,E
Explanation:
dbt is a transformation-focused analytics engineering tool that operates inside the data warehouse. It enables SQL-based data transformations and manages dependencies between models (Answer D). dbt also provides built-in data testing capabilities, allowing teams to define and run tests for data quality, such as uniqueness, not-null constraints, and referential integrity checks (Answer B).
dbt does not handle data loading, visualization, or replication; those functions are typically handled by ingestion tools, BI platforms, or replication services. SnowPro Architect candidates are expected to understand dbt's role in the Snowflake ecosystem and how it fits into modern ELT architectures.
NEW QUESTION # 162
Based on the Snowflake object hierarchy, what securable objects belong directly to a Snowflake account?
(Select THREE).
Answer: B,D,E
Explanation:
A securable object is an entity to which access can be granted in Snowflake. Securable objects include databases, schemas, tables, views, stages, pipes, functions, procedures, sequences, tasks, streams, roles, warehouses, and shares1.
The Snowflake object hierarchy is a logical structure that organizes the securable objects in a nested manner.
The top-most container is the account, which contains all the databases, roles, and warehouses for the customer organization. Each database contains schemas, which in turn contain tables, views, stages, pipes, functions, procedures, sequences, tasks, and streams. Each role can be granted privileges on other roles or securable objects. Each warehouse can be used to execute queries on securable objects2.
Based on the Snowflake object hierarchy, the securable objects that belong directly to a Snowflake account are databases, roles, and warehouses. These objects are created and managed at the account level, and do not depend on any other securable object. The other options are not correct because:
Schemas belong to databases, not to accounts. A schema must be created within an existing database3.
Tables belong to schemas, not to accounts. A table must be created within an existing schema4.
Stages belong to schemas or tables, not to accounts. A stage must be created within an existing schema or table.
1: Overview of Access Control | Snowflake Documentation
2: Securable Objects | Snowflake Documentation
3: CREATE SCHEMA | Snowflake Documentation
4: CREATE TABLE | Snowflake Documentation
[5]: CREATE STAGE | Snowflake Documentation
NEW QUESTION # 163
Which Snowflake objects can be used in a data share? (Select TWO).
Answer: B,C
Explanation:
https://docs.snowflake.com/en/user-guide/data-sharing-intro
NEW QUESTION # 164
......
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