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| Section | Weight | Objectives |
|---|---|---|
| Performance Optimization, Querying & Transformation | 21% | - Materialized Views, Streams, Tasks, Pipelines - Cortex AI, Notebooks, Apache Iceberg Tables - SQL Transformations, Semi-structured/Unstructured Data - Query Processing, Profiling, Optimization |
| Data Loading, Unloading & Connectivity | 18% | - Unloading Data, Export Options - Stages, File Formats, COPY INTO, Snowpipe - External Integrations, Connectors, SnowSQL - Bulk Load, Streaming, Dynamic Tables |
| Data Collaboration & Protection | 10% | - Data Protection, Retention, Time Travel, Fail-safe - Secure Data Sharing, Listings, Marketplace - Data Clean Rooms, Collaboration Features |
| Account Management & Data Governance | 20% | - Governance: Resource Monitors, Data Masking, Row/Column Security - Security: Network Policies, MFA, Encryption - Access Control: RBAC, DAC, Roles, Privileges - Trust Center, Data Lineage, Compliance |
| Snowflake Architecture & Core Concepts | 31% | - Snowsight Interface, Core Platform Features - Databases, Schemas, Objects, Data Types - Virtual Warehouses, Clustering, Caching - Cloud Services Layer, Compute Layer, Storage Layer |
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NEW QUESTION # 386
Which Snowflake database object can be shared with other accounts?
Answer: C
Explanation:
In Snowflake, Secure User-Defined Functions (UDFs) can be shared with other accounts using Snowflake ' s data sharing feature. This allows different Snowflake accounts to securely execute the UDFs without having direct access to the underlying data the functions operate on, ensuring privacy and security. The sharing is facilitated through shares created in Snowflake, which can contain Secure UDFs along with other database objects like tables and views.
References: Snowflake Documentation on Data Sharing and Secure UDFs
NEW QUESTION # 387
What is the MINIMUM size of a table for which Snowflake recommends considering adding a clustering key?
Answer: C
Explanation:
Snowflake recommends considering adding a clustering key to a table when its size reaches1 Terabyte (TB)or larger. Clustering keys help optimize the storage and query performance by organizing the data in a table based on the specified columns. This is particularly beneficial for large tables where data retrieval can become inefficient without proper clustering.
Why Clustering Keys Are Important:Clustering keys ensure that data stored in Snowflake is physically ordered in a way that aligns with the most frequent access patterns, thereby reducing the amount of scanned data during queries and improving performance.
Recommendation Basis:The recommendation for tables of size 1 TB or larger is based on the observation that smaller tables generally do not benefit as much from clustering, given Snowflake ' s architecture. However, as tables grow in size, the benefits of clustering become more pronounced.
Implementing Clustering Keys:
To set a clustering key for a table, you can use theCLUSTER BYclause during table creation or alter an existing table to add it:
CREATETABLEmy_table (... ) CLUSTERBY(column1, column2);
Or for an existing table:
ALTERTABLEmy_table CLUSTERBY(column1, column2);
Reference:For additional information on clustering in Snowflake and recommendations on when to use clustering keys, consult the Snowflake documentation on managing and optimizing data clustering:
https://docs.snowflake.com/en/user-guide/tables-clustering-keys.html
NEW QUESTION # 388
Which feature allows a user to review the monthly projected costs of a Snowflake resource against the monthly actual costs?
Answer: D
Explanation:
Snowflake Budgets is the cost-management feature designed around a monthly spending limit. It tracks credit consumption for an account or a defined group of supported objects, presents spending against the configured monthly budget, and forecasts whether current usage is likely to exceed that limit.
Notifications and custom actions can be based on projected or actual spending. Auto-suspend controls warehouse idle time, a scaling policy governs multi-cluster behavior, and a resource monitor tracks warehouse credit usage and can notify or suspend warehouses at thresholds. Those features do not provide the same monthly actual-versus-projected budget view. Therefore, Budgets is the appropriate feature for reviewing current spending and projected monthly cost for a Snowflake resource or resource group.
NEW QUESTION # 389
When used with the UNLOAD command, which parameter specifies the destination of unloaded data?
Answer: D
Explanation:
In Snowflake, the COPY INTO <stage name> syntax is used with the UNLOAD command to specify the target location where the data should be unloaded, typically a stage or cloud storage (such as Amazon S3 or Azure Blob Storage). This command unloads data from a Snowflake table into files within the specified destination, enabling easy export and external storage of data. GET and PUT commands are used for file management but are not related to unloading table data directly.
NEW QUESTION # 390
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 # 391
......
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