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| Section | Objectives |
|---|---|
| Data Loading and Unloading | - External and internal stages - Bulk data loading using stages and COPY INTO |
| Performance and Optimization | - Query optimization basics - Warehouse sizing and scaling |
| Security and Access Control | - Role-based access control (RBAC) - Authentication and user management |
| Data Transformation and Processing | - SQL operations in Snowflake - Streams and tasks for data pipelines |
| Snowflake Architecture and Platform Concepts | - Virtual warehouses and compute model - Snowflake architecture fundamentals - Storage and data organization |
| Data Sharing and Protection | - Data replication and failover concepts - Secure data sharing |
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NEW QUESTION # 793
What Snowflake role must be granted for a user to create and manage accounts?
Answer: A
Explanation:
The ACCOUNTADMIN role is required for a user to create and manage accounts in Snowflake.This role has the highest level of privileges and is responsible for managing all aspects of the Snowflake account, including the ability to create and manage other user accounts1.
https://docs.snowflake.com/en/user-guide/security-access-control-considerations.html
NEW QUESTION # 794
What is a key difference between JSON and CSV file formats when loading data? Select TWO.
Answer: A,B
Explanation:
The correct answers are A. CSV stores flat, tabular data and E. JSON can be loaded into a VARIANT column without predefined schema .
Important correction:
The provided answer A, B is not correct. CSV does not natively support complex semi-structured data types such as arrays and objects. JSON does.
Why A is correct:
CSV is a delimited, flat file format commonly used for structured, tabular data. Each row usually represents a record, and each field is separated by a delimiter such as a comma.
Why E is correct:
JSON is semi-structured data and can be loaded into a Snowflake VARIANT column. This allows Snowflake to store JSON objects and arrays without requiring every nested field to be predefined as a relational column.
Example:
CREATE TABLE json_data (
src VARIANT
);
COPY INTO json_data
FROM @my_stage
FILE_FORMAT = (TYPE = JSON);
Why the other options are incorrect:
B). CSV does not natively support arrays and objects. JSON supports nested structures such as arrays and objects.
C). JSON does not need to be converted to CSV before it can be queried in Snowflake. Snowflake can query JSON data stored in VARIANT, OBJECT, or ARRAY columns.
D). CSV files do not automatically detect and apply Snowflake table data types during loading. The target table schema and file format options control how data is interpreted.
Official Snowflake documentation reference:
Snowflake documentation explains that CSV is used for structured data, while semi-structured formats such as JSON can be loaded into VARIANT columns and queried using semi-structured data syntax.
Reference: Snowflake Documentation - Semi-structured data; Snowflake Documentation - Loading JSON data; Snowflake Documentation - CSV file format options; SnowPro Core Study Guide - Data Loading and Unloading.
NEW QUESTION # 795
Which of the following describes external functions in Snowflake?
Answer: B
Explanation:
External functions in Snowflake are a special type of User-Defined Function (UDF) that call code executed outside of Snowflake, typically through a remote service. Unlike traditional UDFs, external functions do not contain SQL code within Snowflake; instead, they interact with external services to process data2.
https://docs.snowflake.com/en/sql-reference/external-functions.html#:~:text=External%20functions%20are%
20user%2Ddefined,code%20running%20outside%20of%20Snowflake.
NEW QUESTION # 796
What SQL command would be used to view all roles that were granted to user.1?
Answer: D
Explanation:
The correct command to view all roles granted to a specific user in Snowflake is SHOW GRANTS TO USER
< user_name > ;. This command lists all access control privileges that have been explicitly granted to the specified user. References: SHOW GRANTS | Snowflake Documentation
NEW QUESTION # 797
The effects of query pruning can be observed by evaluating which statistics? (Select TWO).
Answer: B,D
Explanation:
Query pruning in Snowflake refers to the optimization technique where the system reduces the amount of data scanned by a query based on the query conditions. This typically involves skipping unnecessary data partitions that do not contribute to the query result. The effectiveness of this technique can be observed through:
Option A: Partitions scanned. This statistic indicates how many data partitions were actually scanned as a result of query pruning, showing the optimization in action.
Option C: Bytes scanned. This measures the volume of data physically read during query execution, and a reduction in this number indicates effective query pruning, as fewer bytes are read when unnecessary partitions are skipped.
Options B, D, and E do not directly relate to observing the effects of query pruning. " Partitions total " shows the total available, not the impact of pruning, while " Bytes read from result " and " Bytes written " relate to output rather than the efficiency of data scanning.
References:Snowflake documentation on performance tuning and query optimization techniques, specifically how query pruning affects data access.
NEW QUESTION # 798
......
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