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| Section | Weight | Objectives |
|---|---|---|
| Data Sharing | 12% | - Data listing marketplace - Shares and reader accounts - Reader account management - Private sharing vs. Data Exchange |
| Semi-Structured Data | 10% | - VARIANT data type - Parsing and flattening semi-structured data - Array and object operations |
| Data Loading & Unloading | 15% | - Continuous loading with Snowpipe - Data unloading and staging - Data ingestion best practices - Bulk loading with COPY command - File formats (JSON, CSV, Parquet, AVRO) |
| Performance Optimization & Monitoring | 4% | - Query history and monitoring - Query profiling and performance tuning - Resource optimization techniques |
| Account Objects | 8% | - Organizations and accounts structure - Warehouses, databases, and schemas hierarchy - Resource monitors |
| Databases, Tables & Views | 18% | - Data manipulation (DML operations) - Clustering and micro-partitions - Stored procedures and UDFs - Column types and sequences - Table types (permanent, transient, temporary) - Views (standard, materialized, secure) |
| Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake architecture layers (storage, compute, services) - Snowflake editions and features - Virtual warehouse concepts and credit usage |
| Virtual Warehouses | 15% | - Warehouse sizing and scaling - Warehouse creation and configuration - Multi-cluster warehouses - Warehouse monitoring and troubleshooting - Cache concepts (warehouse, result, metadata) |
| Security & Access Control | 10% | - Authorization (roles, privileges) - Authentication (SSO, MFA) - Governance best practices - Row-level and column-level security - Masking policies |
| Time Travel & Cloning | 10% | - Fail-safe concepts - UNDROP and historical data access - Time travel concepts and retention - Zero-copy cloning |
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NEW QUESTION # 864
Queries are frequently run on very large tables and return only a few rows.
Use of which feature or service will optimize the query performance?
Answer: B
Explanation:
Search Optimization Service enhances performance for selective queries that return a small number of rows from large datasets. It avoids full table scans.
Reference:
Snowflake Docs: Search Optimization Service
NEW QUESTION # 865
Which features could be used to improve the performance of queries that return a small subset of rows from a large table? (Select TWO).
Answer: A,B
NEW QUESTION # 866
Which clients does Snowflake support Multi-Factor Authentication (MFA) token caching for? (Select TWO).
Answer: B,D
Explanation:
Multi-Factor Authentication (MFA) token caching is typically supported for clients that maintain a persistent connection or session with Snowflake, such as the ODBC driver and Python connector, to reduce the need for repeated MFA challenges.References:Based on general security practices in cloud services as of 2021.
NEW QUESTION # 867
What features does Snowflake Time Travel enable?
Answer: A,C
Explanation:
Snowflake Time Travel is a powerful feature that allows users to access historical data within a defined period. It enables two key capabilities:
B . Restoring data-related objects that have been deleted within the past 90 days: Time Travel can be used to restore tables, schemas, and databases that have been accidentally or intentionally deleted within the Time Travel retention period.
C . Conducting point-in-time analysis for BI reporting: It allows users to query historical data as it appeared at a specific point in time within the Time Travel retention period, which is crucial for business intelligence and reporting purposes.
While Time Travel does allow querying of past data, it is limited to the retention period set for the Snowflake account, which is typically 1 day for standard accounts and can be extended up to 90 days for enterprise accounts. It does not enable querying or restoring objects created or deleted beyond the retention period, nor does it provide analysis over all periods of time.
References:
Snowflake Documentation on Time Travel
SnowPro Core Certification Study Guide
NEW QUESTION # 868
From what stage can a Snowflake user omit the FROM clause while loading data into a table?
Answer: A
Explanation:
In Snowflake, when loading data into a table using the COPY INTO command, the FROM clause can be omitted if loading from the table's stage, also known as the table stage. The table stage is a default location associated with each table where files can be temporarily stored for loading operations. This simplifies the data loading process by allowing direct loading from files that have been uploaded to the table's stage without specifying the stage explicitly in the COPY INTO command.
References:
Snowflake Documentation: Loading Data into Tables
NEW QUESTION # 869
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