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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Databases, Tables & Views | 18% | - Data manipulation (DML operations) - Views (standard, materialized, secure) - Clustering and micro-partitions - Stored procedures and UDFs - Table types (permanent, transient, temporary) - Column types and sequences |
| Topic 2: Security & Access Control | 10% | - Governance best practices - Authentication (SSO, MFA) - Row-level and column-level security - Authorization (roles, privileges) - Masking policies |
| Topic 3: Virtual Warehouses | 15% | - Cache concepts (warehouse, result, metadata) - Warehouse sizing and scaling - Multi-cluster warehouses - Warehouse creation and configuration - Warehouse monitoring and troubleshooting |
| Topic 4: Data Sharing | 12% | - Private sharing vs. Data Exchange - Reader account management - Shares and reader accounts - Data listing marketplace |
| Topic 5: Account Objects | 8% | - Resource monitors - Warehouses, databases, and schemas hierarchy - Organizations and accounts structure |
| Topic 6: Time Travel & Cloning | 10% | - UNDROP and historical data access - Fail-safe concepts - Zero-copy cloning - Time travel concepts and retention |
| Topic 7: Performance Optimization & Monitoring | 4% | - Resource optimization techniques - Query history and monitoring - Query profiling and performance tuning |
| Topic 8: Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake architecture layers (storage, compute, services) - Virtual warehouse concepts and credit usage - Snowflake editions and features |
| Topic 9: Semi-Structured Data | 10% | - Parsing and flattening semi-structured data - Array and object operations - VARIANT data type |
| Topic 10: Data Loading & Unloading | 15% | - Bulk loading with COPY command - Continuous loading with Snowpipe - File formats (JSON, CSV, Parquet, AVRO) - Data unloading and staging - Data ingestion best practices |
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NEW QUESTION # 1005
What objects are required to unload data into cloud data storage? (Select TWO).
Answer: A,E
NEW QUESTION # 1006
What is a benefit of using Snowflake directory tables?
Answer: B
Explanation:
The correct answer is D. Directory tables can be joined with other tables in Snowflake .
A directory table stores file-level metadata for files in a stage. This metadata can be queried using SQL, which means it can also be filtered, selected from, and joined with other Snowflake tables.
Why D is correct:
Because directory table metadata can be queried like table data, users can join that metadata with other Snowflake tables. This is useful for workflows where file metadata needs to be compared with control tables, audit tables, or processing logs.
Example concept:
SELECT d.relative_path, l.load_status
FROM DIRECTORY(@my_stage) d
LEFT JOIN load_log l
ON d.relative_path = l.file_name;
Why the other options are incorrect:
A). Directory tables store file-level metadata, but not in a temporary table. They are associated with stages.
B). Directory tables do not have their own independent privilege model. Access is controlled through privileges on the associated stage.
C). Any user cannot configure directory table privileges independently.
Official Snowflake documentation reference:
Snowflake documentation explains that directory tables store file-level metadata about staged files and that users can query this metadata with SQL, enabling joins with other tables.
Reference: Snowflake Documentation - Directory tables; Snowflake Documentation - Stages; SnowPro Core Study Guide - Data Loading and Unloading.
NEW QUESTION # 1007
Which data type can be used for floating-point numbers without losing precision?
Answer: C
Explanation:
In Snowflake, the DOUBLE data type is used for floating-point numbers and can represent a wide range of values without losing precision. This data type is ideal for storing numerical values that require decimal precision.
Define the Column: When creating a table, specify the column with the DOUBLE data type to store floating-point numbers.
CREATE TABLE example_table (
id INTEGER,
value DOUBLE
);
Insert Data: Insert floating-point numbers into the DOUBLE column.
INSERT INTO example_table (id, value) VALUES (1, 123.456);
References:
Snowflake Documentation: Data Types
Snowflake Documentation: Numeric Data Types
NEW QUESTION # 1008
Which Snowflake service is managed within the Cloud Services layer?
Answer: C
Explanation:
The correct answer is D. Infrastructure management .
Snowflake's architecture includes the storage layer, compute layer, and Cloud Services layer. The Cloud Services layer coordinates many system-level services required to operate Snowflake.
Why D is correct:
Infrastructure management is handled by the Cloud Services layer. Other Cloud Services responsibilities include authentication, metadata management, query parsing and optimization, access control, and transaction management.
Why the other options are incorrect:
A). Data storage is handled by the database storage layer.
B). File compression is related to storage and data loading/unloading behavior, not the primary Cloud Services responsibility tested here.
C). Query processing is performed by virtual warehouses in the compute layer. Query parsing and optimization happen in Cloud Services, but execution/processing happens in compute.
Official Snowflake documentation reference:
Snowflake documentation describes the Cloud Services layer as managing services such as authentication, infrastructure management, metadata management, query parsing and optimization, access control, and transaction management.
Reference: Snowflake Documentation - Snowflake Architecture; Cloud Services layer; SnowPro Core Study Guide - Snowflake Architecture.
NEW QUESTION # 1009
What privilege is required to view the load statistics on a virtual warehouse?
Answer: C
NEW QUESTION # 1010
......
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