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| Section | Objectives |
|---|---|
| Topic 1: Snowflake Architecture and Platform Concepts | - Snowflake architecture fundamentals - Storage and data organization - Virtual warehouses and compute model |
| Topic 2: Performance and Optimization | - Query optimization basics - Warehouse sizing and scaling |
| Topic 3: Data Sharing and Protection | - Secure data sharing - Data replication and failover concepts |
| Topic 4: Security and Access Control | - Role-based access control (RBAC) - Authentication and user management |
| Topic 5: Data Loading and Unloading | - External and internal stages - Bulk data loading using stages and COPY INTO |
| Topic 6: Data Transformation and Processing | - Streams and tasks for data pipelines - SQL operations in Snowflake |
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NEW QUESTION # 817
What is the purpose of key-pair authentication?
Answer: B
Explanation:
The correct answer is C. To securely authenticate a user or an application .
Key-pair authentication uses a public/private key pair to authenticate to Snowflake. It is commonly used for service accounts, automation, applications, and client tools that need secure authentication without relying on passwords.
Why C is correct:
In key-pair authentication, the private key is used by the client, and the public key is associated with the Snowflake user. Snowflake verifies the authentication request using the public key.
Why the other options are incorrect:
A). Key-pair authentication is about authentication, not encrypting Snowflake data for access by one of two keys.
B). Secure data sharing is a separate Snowflake feature and is not the purpose of key-pair authentication.
D). MFA is a separate authentication control. Key-pair authentication does not itself enforce MFA.
Official Snowflake documentation reference:
Snowflake documentation describes key-pair authentication as an authentication method using a public
/private key pair for users and applications.
Reference: Snowflake Documentation - Key-pair authentication; Snowflake Documentation - User authentication; SnowPro Core Study Guide - Security and Access Control.
NEW QUESTION # 818
A table was created using this statement:
Which feature of this table will incur both compute and storage costs?
Answer: D
NEW QUESTION # 819
A table is created with a column set to a data type of CHARACTER(25). If the table is viewed in the database section of Snowsight, what data type will be shown for the column?
Answer: D
Explanation:
The correct answer is C. VARCHAR(25) .
In Snowflake, CHAR, CHARACTER, STRING, and TEXT are synonyms for VARCHAR. Snowflake stores and displays these character string data types as VARCHAR.
Corrected typo:
Option D was written as CHARACTERS). The corrected option is CHARACTER(25) .
Why C is correct:
A column created as CHARACTER(25) is treated as VARCHAR(25) in Snowflake metadata and display output.
Why the other options are incorrect:
A). STRING is a synonym for VARCHAR, but Snowflake commonly displays the canonical type as VARCHAR.
B). TEXT is also a synonym for VARCHAR, but it is not the canonical display type.
D). CHARACTER is accepted when defining the column, but Snowflake displays it as VARCHAR.
Official Snowflake documentation reference:
Snowflake documentation explains that string data type synonyms such as CHAR, CHARACTER, STRING, and TEXT are equivalent to VARCHAR.
Reference: Snowflake Documentation - String and binary data types; SnowPro Core Study Guide - SQL and Snowflake Objects.
NEW QUESTION # 820
Which function can be used to convert semi-structured data into a relational representation?
Answer: C
NEW QUESTION # 821
What is a User-Defined Table Function, or UDTF, used for in Snowflake?
Answer: A
Explanation:
The correct answer is A. To return a tabular value for each input row.
A User-Defined Table Function, or UDTF, returns a set of rows instead of a single scalar value. This is different from a scalar UDF, which returns one value for each input row.
Why A is correct:
A UDTF produces tabular output. It can return multiple rows and multiple columns, depending on how the function is defined.
Example concept:
SELECT *
FROM TABLE(my_udtf(input_column));
This syntax shows that the function result is treated like a table.
Why the other options are incorrect:
B). Returning a pandas Series is associated with vectorized Python UDF behavior, not the general definition of a UDTF.
C). Returning one output row as a single column or value describes a scalar UDF, not a table function.
D). Processing input rows as pandas DataFrames is associated with Snowpark Python and vectorized processing patterns, but it is not the basic Snowflake definition of a UDTF.
Official Snowflake documentation reference:
Snowflake documentation defines a UDTF as a user-defined function that returns tabular results. It can return multiple rows for each input row.
Reference: Snowflake Documentation - User-defined table functions; Snowflake Documentation - User- defined functions; SnowPro Core Study Guide - SQL and Snowflake Objects.
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NEW QUESTION # 822
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