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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Time Travel & Cloning | 10% | - Zero-copy cloning - Time travel concepts and retention - UNDROP and historical data access - Fail-safe concepts |
| Topic 2: Account Objects | 8% | - Organizations and accounts structure - Resource monitors - Warehouses, databases, and schemas hierarchy |
| Topic 3: Semi-Structured Data | 10% | - Array and object operations - VARIANT data type - Parsing and flattening semi-structured data |
| Topic 4: Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake editions and features - Snowflake architecture layers (storage, compute, services) - Virtual warehouse concepts and credit usage |
| Topic 5: Data Sharing | 12% | - Private sharing vs. Data Exchange - Shares and reader accounts - Data listing marketplace - Reader account management |
| Topic 6: Data Loading & Unloading | 15% | - Data unloading and staging - Data ingestion best practices - Continuous loading with Snowpipe - Bulk loading with COPY command - File formats (JSON, CSV, Parquet, AVRO) |
| Topic 7: Security & Access Control | 10% | - Authentication (SSO, MFA) - Authorization (roles, privileges) - Masking policies - Governance best practices - Row-level and column-level security |
| Topic 8: Performance Optimization & Monitoring | 4% | - Query history and monitoring - Resource optimization techniques - Query profiling and performance tuning |
| Topic 9: Databases, Tables & Views | 18% | - Clustering and micro-partitions - Data manipulation (DML operations) - Stored procedures and UDFs - Views (standard, materialized, secure) - Column types and sequences - Table types (permanent, transient, temporary) |
| Topic 10: Virtual Warehouses | 15% | - Warehouse creation and configuration - Warehouse sizing and scaling - Warehouse monitoring and troubleshooting - Cache concepts (warehouse, result, metadata) - Multi-cluster warehouses |
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NEW QUESTION # 902
Which functions are used to aggregate values into an array? Select TWO.
Answer: E
Explanation:
The clearly correct answer from the listed options is A. ARRAY_AGG .
Important correction:
The provided answer A, D is not fully correct if the question is asking about aggregate functions .
ARRAY_CONSTRUCT creates an array from explicitly supplied values, but it does not aggregate values across multiple rows.
Why A is correct:
ARRAY_AGG is an aggregate function. It aggregates input values from multiple rows into a single array.
Example:
SELECT ARRAY_AGG(product_id)
FROM orders;
This returns an array containing product_id values from multiple rows.
Why D is not an aggregate function:
ARRAY_CONSTRUCT creates an array from the values passed into the function.
Example:
SELECT ARRAY_CONSTRUCT( ' A ' , ' B ' , ' C ' );
This creates an array from listed expressions, but it does not aggregate values across rows.
Why the other options are incorrect:
B). ARRAY_SLICE returns a subset of an existing array.
C). ARRAY_APPEND appends a value to an existing array.
E). ARRAY_TO_STRING converts an array to a string.
Official Snowflake documentation reference:
Snowflake documentation describes ARRAY_AGG as an aggregate function that returns the input values, pivoted into an array. ARRAY_CONSTRUCT is documented as a scalar function that constructs an array from input expressions.
Reference: Snowflake Documentation - ARRAY_AGG; Snowflake Documentation - ARRAY_CONSTRUCT; SnowPro Core Study Guide - Working with Semi-Structured Data.
NEW QUESTION # 903
What is the purpose of an External Function?
Answer: C
Explanation:
The purpose of an External Function in Snowflake is to call code that executes outside of the Snowflake environment.This allows Snowflake to interact with external services and leverage functionalities that are not natively available within Snowflake, such as calling APIs or running custom code hosted on cloud services3.
https://docs.snowflake.com/en/sql-reference/external-functions.html
NEW QUESTION # 904
A team plans to build data-transformation pipelines using a Snowflake-supported development framework that provides compute pushdown and native integration with Snowflake features.
Which feature should the team use?
Answer: C
Explanation:
Snowpark provides language-specific APIs for building data-processing and transformation logic. Snowpark operations are translated into SQL and executed by Snowflake's compute engine. This provides compute pushdown because processing occurs inside Snowflake instead of requiring the complete dataset to be transferred to an external application.
The JDBC and ODBC drivers provide database connectivity. They allow applications to submit SQL statements and retrieve results, but they are not transformation frameworks comparable to Snowpark.
The Snowflake Connector for Kafka is primarily used to ingest streaming data from Apache Kafka into Snowflake.
Correction: The original option A said "Snowflake Connector for Python." That connector is a Python database driver. The feature that provides DataFrame-based transformations and compute pushdown is the Snowpark API for Python.
References:
Snowflake Documentation - Snowpark Developer Guide for Python: https://docs.snowflake.com/en
/developer-guide/snowpark/python/index
Snowflake Documentation - Snowpark API overview: https://docs.snowflake.com/en/developer-guide
/snowpark/index
==
NEW QUESTION # 905
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 # 906
When unloading data to an external stage, what is the MAXIMUM file size supported?
Answer: C
Explanation:
When unloading data to an external stage, the maximum file size supported is 5 GB.This limit ensures efficient data transfer and management within Snowflake's architecture
NEW QUESTION # 907
......
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