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| Section | Weight | Objectives |
|---|---|---|
| Virtual Warehouses | 15% | - Cache concepts (warehouse, result, metadata) - Warehouse sizing and scaling - Multi-cluster warehouses - Warehouse creation and configuration - Warehouse monitoring and troubleshooting |
| Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake editions and features - Virtual warehouse concepts and credit usage - Snowflake architecture layers (storage, compute, services) |
| Databases, Tables & Views | 18% | - Table types (permanent, transient, temporary) - Stored procedures and UDFs - Views (standard, materialized, secure) - Clustering and micro-partitions - Column types and sequences - Data manipulation (DML operations) |
| Semi-Structured Data | 10% | - Parsing and flattening semi-structured data - VARIANT data type - Array and object operations |
| Security & Access Control | 10% | - Masking policies - Row-level and column-level security - Governance best practices - Authorization (roles, privileges) - Authentication (SSO, MFA) |
| Time Travel & Cloning | 10% | - UNDROP and historical data access - Fail-safe concepts - Zero-copy cloning - Time travel concepts and retention |
| Data Loading & Unloading | 15% | - Continuous loading with Snowpipe - Data unloading and staging - File formats (JSON, CSV, Parquet, AVRO) - Data ingestion best practices - Bulk loading with COPY command |
| Data Sharing | 12% | - Reader account management - Shares and reader accounts - Data listing marketplace - Private sharing vs. Data Exchange |
| Performance Optimization & Monitoring | 4% | - Query history and monitoring - Resource optimization techniques - Query profiling and performance tuning |
| Account Objects | 8% | - Resource monitors - Warehouses, databases, and schemas hierarchy - Organizations and accounts structure |
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NEW QUESTION # 1080
Which file format will keep floating-point numbers from being truncated when data is unloaded?
Answer: A
Explanation:
The Parquet file format is known for preserving the precision of floating-point numbers when data is unloaded, preventing truncation of the values3.
NEW QUESTION # 1081
A sales table FCT_SALES has 100 million records.
The following Query was executed
SELECT COUNT (1) FROM FCT__SALES;
How did Snowflake fulfill this query?
Answer: A
Explanation:
Snowflake is designed to optimize query performance by utilizing metadata for certain types of queries.
When executing a COUNT query, Snowflake can often fulfill the request by accessing metadata about the table's row count, rather than scanning the entire table or micro-partitions. This is particularly efficient for large tables like FCT_SALES with a significant number of records. The metadata layer maintains statistics about the table, including the row count, which enables Snowflake to quickly return the result of a COUNT query without the need to perform a full scan.
References:
Snowflake Documentation on Metadata Management
SnowPro Core Certification StudyGuide
NEW QUESTION # 1082
A marketing co-worker has requested the ability to change a warehouse size on their medium virtual warehouse called mktg__WH.
Which of the following statements will accommodate this request?
Answer: D
Explanation:
:The correct statement to accommodate the request for a marketing co-worker to change the size of their medium virtual warehouse called mktg__WH is to grant the MODIFY privilege on the warehouse to the ROLE MARKETING. This privilege allows the role to change the warehouse size among other properties.
References:
[COF-C02] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Access Control Privileges1
NEW QUESTION # 1083
What transformations are supported in a CREATE PIPE ... AS COPY ... FROM (....) statement? (Select TWO.)
Answer: C,D
Explanation:
In a CREATE PIPE ... AS COPY ... FROM (....) statement, the supported transformations include filtering data using an optional WHERE clause and omitting columns. The WHERE clause allows for the specification of conditions to filter the data that is being loaded, ensuring only relevant data is inserted into the table.
Omitting columns enables the exclusion of certain columns from the data load, which can be useful when the incoming data contains more columns than are needed for the target table.
References:
[ COF-C03 ] SnowPro Core Certification Exam Study Guide
Simple Transformations During a Load1
NEW QUESTION # 1084
Network policies can be set at which Snowflake levels? (Choose two.)
Answer: A,E
Explanation:
Network policies in Snowflake can be set at the user level and at the account level2.
Reference: https://docs.snowflake.com/en/user-guide/network-policies.html#creating-network-policies
NEW QUESTION # 1085
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