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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Databases, Tables & Views | 18% | - Views (standard, materialized, secure) - Data manipulation (DML operations) - Clustering and micro-partitions - Column types and sequences - Table types (permanent, transient, temporary) - Stored procedures and UDFs |
| Topic 2: Account Objects | 8% | - Organizations and accounts structure - Resource monitors - Warehouses, databases, and schemas hierarchy |
| Topic 3: Time Travel & Cloning | 10% | - Zero-copy cloning - UNDROP and historical data access - Fail-safe concepts - Time travel concepts and retention |
| Topic 4: Performance Optimization & Monitoring | 4% | - Query history and monitoring - Resource optimization techniques - Query profiling and performance tuning |
| Topic 5: Virtual Warehouses | 15% | - Cache concepts (warehouse, result, metadata) - Warehouse monitoring and troubleshooting - Multi-cluster warehouses - Warehouse creation and configuration - Warehouse sizing and scaling |
| Topic 6: Cloud Platform Overview & Snowflake Key Concepts | 8% | - Virtual warehouse concepts and credit usage - Snowflake editions and features - Snowflake architecture layers (storage, compute, services) |
| Topic 7: Semi-Structured Data | 10% | - VARIANT data type - Array and object operations - Parsing and flattening semi-structured data |
| Topic 8: Security & Access Control | 10% | - Authentication (SSO, MFA) - Row-level and column-level security - Authorization (roles, privileges) - Masking policies - Governance best practices |
| Topic 9: Data Loading & Unloading | 15% | - File formats (JSON, CSV, Parquet, AVRO) - Data unloading and staging - Bulk loading with COPY command - Data ingestion best practices - Continuous loading with Snowpipe |
| Topic 10: Data Sharing | 12% | - Data listing marketplace - Private sharing vs. Data Exchange - Reader account management - Shares and reader accounts |
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NEW QUESTION # 883
Which operation can be performed on Snowflake external tables?
Answer: D
Explanation:
Snowflake external tables are read-only, which means data manipulation language (DML) operations like INSERT, RENAME, or ALTER cannot be performed on them. However, external tables can be used for query and join operations3.
References: [ COF-C03 ] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 884
A Snowflake user needs to optimize the definition of a secure view, but the user cannot see the view.
Which of the LEAST-PRIVILEGED access or role that should be granted to the user to complete this task?
Answer: A
NEW QUESTION # 885
How can a user optimize the performance of a COPY INTO < table > query that is being run on a very large file?
Answer: C
Explanation:
The correct answer is A. Split the file into smaller files .
Snowflake can load files in parallel. A single very large file limits parallelism because fewer worker threads can process the load concurrently. Splitting a very large file into multiple smaller files allows Snowflake to load the files in parallel, improving performance.
Why A is correct:
For best loading performance, Snowflake recommends splitting large data files into smaller files of a recommended size range so that the load operation can be parallelized.
Why the other options are incorrect:
B). Increasing the warehouse size can sometimes help, but if the load is constrained by a single very large file, file splitting is the better optimization.
C). SIZE_LIMIT controls the maximum amount of data loaded during a COPY command. It does not improve performance for a very large file.
D). COMPRESSION = AUTO lets Snowflake detect compression type. It does not solve the lack of parallelism caused by a single large file.
Official Snowflake documentation reference:
Snowflake documentation recommends splitting large files into smaller files to increase parallelism and improve load performance.
Reference: Snowflake Documentation - Data loading considerations; Snowflake Documentation - COPY INTO < table > ; SnowPro Core Study Guide - Data Loading and Unloading.
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NEW QUESTION # 886
Which Snowflake feature is used for both querying and restoring data?
Answer: D
Explanation:
Snowflake's Time Travel feature is used for both querying historical data in tables and restoring and cloning historical data in databases, schemas, and tables3. It allows users to access historical data within a defined period (1 day by default, up to 90 days for Snowflake Enterprise Edition) and is a key feature for data recovery and management.References:[COF-C02] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 887
Which strategy is recommended to improve the performance of a large, complex query?
Answer: D
Explanation:
To improve the performance of compute-intensive queries, increasing the warehouse size (e.g., from Medium to Large or X-Large) provides more compute resources (CPU/memory) , allowing Snowflake to process queries faster. This is the most direct and effective method for performance optimization.
Reference:
Snowflake Docs: Warehouse Sizing
"Larger warehouses provide more compute resources for faster performance."
NEW QUESTION # 888
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