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| Section | Weight | Objectives |
|---|---|---|
| Performance Optimization & Monitoring | 4% | - Query history and monitoring - Query profiling and performance tuning - Resource optimization techniques |
| Virtual Warehouses | 15% | - Cache concepts (warehouse, result, metadata) - Warehouse sizing and scaling - Warehouse creation and configuration - Multi-cluster warehouses - Warehouse monitoring and troubleshooting |
| Semi-Structured Data | 10% | - VARIANT data type - Array and object operations - Parsing and flattening semi-structured data |
| Time Travel & Cloning | 10% | - Time travel concepts and retention - Zero-copy cloning - Fail-safe concepts - UNDROP and historical data access |
| Data Sharing | 12% | - Shares and reader accounts - Reader account management - Data listing marketplace - Private sharing vs. Data Exchange |
| Data Loading & Unloading | 15% | - Data ingestion best practices - Data unloading and staging - Bulk loading with COPY command - Continuous loading with Snowpipe - File formats (JSON, CSV, Parquet, AVRO) |
| Security & Access Control | 10% | - Authentication (SSO, MFA) - Row-level and column-level security - Masking policies - Governance best practices - Authorization (roles, privileges) |
| Databases, Tables & Views | 18% | - Table types (permanent, transient, temporary) - Clustering and micro-partitions - Views (standard, materialized, secure) - Stored procedures and UDFs - Data manipulation (DML operations) - Column types and sequences |
| Cloud Platform Overview & Snowflake Key Concepts | 8% | - Virtual warehouse concepts and credit usage - Snowflake architecture layers (storage, compute, services) - Snowflake editions and features |
| Account Objects | 8% | - Resource monitors - Organizations and accounts structure - Warehouses, databases, and schemas hierarchy |
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NEW QUESTION # 933
How often are the Account and Table master keys automatically rotated by Snowflake?
Answer: A
Explanation:
Snowflake automatically rotates the Account and Table master keys when they are more than 30 days old.Active keys are retired, and new keys are created, ensuring robust security through frequent key changes1
NEW QUESTION # 934
Which data types are supported by the Search Optimization Service? Select TWO.
Answer: A,E
Explanation:
The correct answers are A. INTEGER and B. GEOMETRY .
The Snowflake Search Optimization Service can improve performance for selective queries by maintaining a search access path. It supports several data types depending on the type of search being optimized, including scalar equality searches and geospatial searches.
Why A is correct:
INTEGER is a numeric data type. Search optimization supports equality searches on supported scalar data types, including numeric types such as integers.
Why B is correct:
GEOMETRY is supported for geospatial search optimization. Search optimization can improve performance for supported geospatial predicates when used with geospatial data types.
Why the other options are less appropriate in this question:
C). FLOAT is not the best answer in this option set. Snowflake search optimization support is commonly associated with exact/equality search behavior, and floating-point values are generally less suitable for exact lookup optimization scenarios.
D). VARIANT can be involved in semi-structured search optimization, but this question's provided verified answer indicates the expected choices are INTEGER and GEOMETRY.
E). BOOLEAN is a supported Snowflake scalar type, but it is generally low-cardinality and not the intended answer here.
Official Snowflake documentation reference:
Snowflake documentation explains that the Search Optimization Service supports specific query types and data types, including equality searches on supported scalar values and geospatial search optimization for geospatial data types such as GEOMETRY.
Reference: Snowflake Documentation - Search Optimization Service; Snowflake Documentation - Search optimization for geospatial queries; SnowPro Core Study Guide - Performance Optimization.
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NEW QUESTION # 935
Which command can be used to load data into an internal stage?
Answer: D
Explanation:
The PUT command is used to load data into an internal stage in Snowflake. This command uploads data files from a local file system to a named internal stage, making the data available for subsequent loading into a Snowflake table using the COPY INTO command.
References:
[COF-C02] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Data Loading
NEW QUESTION # 936
At what level is the MIN_DATA_RETENSION_TIME_IN_DAYS parameter set?
Answer: C
Explanation:
The MIN_DATA_RETENTION_TIME_IN_DAYS parameter is set at the Account level in Snowflake. This parameter specifies the minimum number of days Snowflake retains the historical data for time travel, which allows users to access and query data as it existed at previous points in time.
Here's how to understand and adjust this parameter:
Purpose of MIN_DATA_RETENTION_TIME_IN_DAYS: This parameter is crucial for managing data lifecycle and compliance requirements within Snowflake. It determines the minimum time frame for which you can perform operations like restoring deleted objects or accessing historical versions of data.
Setting the Parameter: Only account administrators can set or modify this parameter. It is done at the account level, impacting all databases and schemas within the account. The setting can be adjusted based on the organization's data retention policy.
Adjusting the Parameter:
To view the current setting, use:
SHOW PARAMETERS LIKE 'MIN_DATA_RETENTION_TIME_IN_DAYS';
To change the setting, an account administrator can execute:
ALTER ACCOUNT SET MIN_DATA_RETENTION_TIME_IN_DAYS = <number_of_days>;
NEW QUESTION # 937
What is a responsibility of Snowflake ' s virtual warehouses?
Answer: A
Explanation:
The primary responsibility of Snowflake's virtual warehouses is to execute queries. Virtual warehouses are one of the key components of Snowflake's architecture, providing the compute power required to perform data processing tasks such as running SQL queries, performing joins, aggregations, and other data manipulations.
References:
[ COF-C03 ] SnowPro Core Certification Exam Study Guide
SnowflakeDocumentation on Virtual Warehouses1
NEW QUESTION # 938
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