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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Performance Optimization & Monitoring | 4% | - Query profiling and performance tuning - Resource optimization techniques - Query history and monitoring |
| Topic 2: Security & Access Control | 10% | - Authentication (SSO, MFA) - Governance best practices - Masking policies - Row-level and column-level security - Authorization (roles, privileges) |
| Topic 3: Data Loading & Unloading | 15% | - Bulk loading with COPY command - Continuous loading with Snowpipe - File formats (JSON, CSV, Parquet, AVRO) - Data ingestion best practices - Data unloading and staging |
| Topic 4: Data Sharing | 12% | - Data listing marketplace - Private sharing vs. Data Exchange - Reader account management - Shares and reader accounts |
| Topic 5: Account Objects | 8% | - Organizations and accounts structure - Resource monitors - Warehouses, databases, and schemas hierarchy |
| Topic 6: Semi-Structured Data | 10% | - Array and object operations - VARIANT data type - Parsing and flattening semi-structured data |
| Topic 7: Virtual Warehouses | 15% | - Warehouse creation and configuration - Cache concepts (warehouse, result, metadata) - Multi-cluster warehouses - Warehouse sizing and scaling - Warehouse monitoring and troubleshooting |
| Topic 8: Time Travel & Cloning | 10% | - UNDROP and historical data access - Fail-safe concepts - Zero-copy cloning - Time travel concepts and retention |
| Topic 9: Cloud Platform Overview & Snowflake Key Concepts | 8% | - Virtual warehouse concepts and credit usage - Snowflake editions and features - Snowflake architecture layers (storage, compute, services) |
| Topic 10: Databases, Tables & Views | 18% | - Views (standard, materialized, secure) - Stored procedures and UDFs - Column types and sequences - Clustering and micro-partitions - Table types (permanent, transient, temporary) - Data manipulation (DML operations) |
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NEW QUESTION # 987
What information is included in the output of an EXPLAIN_PLAN?
Answer: A
Explanation:
The correct answer is D. The total number of micro-partitions that the operation will reference .
In Snowflake, EXPLAIN or EXPLAIN USING returns the logical execution plan for a SQL statement. The plan can include information about the operations Snowflake expects to perform, such as table scans, filters, joins, and estimated partitions involved in the query.
Why D is correct:
The explain plan can show partition-related information for table scan operations, including the number of partitions that may be scanned. This is useful for understanding how well Snowflake can prune micro- partitions.
Why the other options are incorrect:
A). EXPLAIN_PLAN does not provide a guaranteed predicted query runtime. Actual execution time depends on warehouse size, load, caching, pruning, and other runtime factors.
B). EXPLAIN_PLAN does not directly state the number of credits the query will consume. Credit consumption is based mainly on warehouse runtime and size.
C). EXPLAIN_PLAN describes the execution plan, not the actual result data type returned by the query.
Official Snowflake documentation reference:
Snowflake documentation explains that the EXPLAIN command displays the logical execution plan for a SQL statement. Query plans can include details about operations such as scans, joins, filters, and partitions.
Reference: Snowflake Documentation - EXPLAIN; Snowflake Documentation - Query Profile and query optimization; SnowPro Core Study Guide - Performance Optimization.
NEW QUESTION # 988
A virtual warehouse has the same value for MIN_CLUSTER_COUNT and MAX_CLUSTER_COUNT. What will happen when the warehouse is started?
Answer: C
Explanation:
The correct answer is A. Snowflake will start all available clusters .
A multi-cluster warehouse can run in Auto-scale mode or Maximized mode. When MIN_CLUSTER_COUNT and MAX_CLUSTER_COUNT are set to the same value greater than 1, the warehouse runs in Maximized mode.
Why A is correct:
In Maximized mode, Snowflake starts all configured clusters when the warehouse starts. For example, if both MIN_CLUSTER_COUNT and MAX_CLUSTER_COUNT are set to 3, Snowflake starts 3 clusters when the warehouse starts.
Clarification:
"Available clusters" here means all clusters configured for that warehouse, not every cluster in the Snowflake account.
Why the other options are incorrect:
B). Starting enough clusters based on workload describes Auto-scale behavior, not Maximized mode.
C). This describes a queued-query scaling behavior but does not apply when minimum and maximum cluster counts are equal.
D). This describes the Economy scaling policy behavior in Auto-scale mode, not Maximized mode.
Official Snowflake documentation reference:
Snowflake documentation explains that when the minimum and maximum number of clusters are the same, a multi-cluster warehouse runs in Maximized mode and starts all clusters when the warehouse starts.
Reference: Snowflake Documentation - Multi-cluster warehouses; Snowflake Documentation - Maximized mode; SnowPro Core Study Guide - Snowflake Account and Resource Management.
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NEW QUESTION # 989
What is the purpose of the STRIP NULL_VALUES file format option when loading semi-structured data files into Snowflake?
Answer: B
Explanation:
The STRIP NULL_VALUES file format option, when set to TRUE, removes object or array elements that contain null values during the loading process of semi-structured data files into Snowflake.This ensures that the data loaded into Snowflake tables does not contain these null elements, which can be useful when the "null" values in files indicate missing values and have no other special meaning2.
References:[COF-C02] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 990
Which type of Snowflake virtual warehouse provides 16 times the memory for each node and is recommended for larger workloads like Machine Learning (ML) training?
Answer: D
Explanation:
The Snowpark-optimized warehouse is designed with significantly higher memory per node, making it ideal for machine learning (ML) training and other memory-intensive workloads. Snowpark-optimized warehouses provide the necessary computational power and memory for complex, large-scale data processing tasks.
Standard and multi-cluster warehouses do not offer the same memory advantage, while the 6XL warehouse is large but not specifically optimized for memory-intensive operations.
NEW QUESTION # 991
Which statistics on a Query Profile reflect the efficiency of the query pruning? (Select TWO).
Answer: B,C
Explanation:
In a Snowflake Query Profile, the statistics "Partitions scanned" and "Bytes scanned" reflect the efficiency of query pruning. Query pruning refers to the ability of the query engine to skip unnecessary data, thereby reducing the amount of data that needs to be processed. Efficient pruning results in fewer partitions and bytes being scanned, improving query performance.
References:
Snowflake Documentation: Understanding Query Profiles
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NEW QUESTION # 992
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