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| Section | Weight | Objectives |
|---|---|---|
| Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake architecture layers (storage, compute, services) - Snowflake editions and features - Virtual warehouse concepts and credit usage |
| Performance Optimization & Monitoring | 4% | - Resource optimization techniques - Query history and monitoring - Query profiling and performance tuning |
| Time Travel & Cloning | 10% | - UNDROP and historical data access - Time travel concepts and retention - Zero-copy cloning - Fail-safe concepts |
| Semi-Structured Data | 10% | - Parsing and flattening semi-structured data - Array and object operations - VARIANT data type |
| Account Objects | 8% | - Warehouses, databases, and schemas hierarchy - Resource monitors - Organizations and accounts structure |
| Data Loading & Unloading | 15% | - Bulk loading with COPY command - Continuous loading with Snowpipe - Data ingestion best practices - Data unloading and staging - File formats (JSON, CSV, Parquet, AVRO) |
| Databases, Tables & Views | 18% | - Data manipulation (DML operations) - Clustering and micro-partitions - Stored procedures and UDFs - Table types (permanent, transient, temporary) - Column types and sequences - Views (standard, materialized, secure) |
| Security & Access Control | 10% | - Governance best practices - Authorization (roles, privileges) - Masking policies - Row-level and column-level security - Authentication (SSO, MFA) |
| Data Sharing | 12% | - Private sharing vs. Data Exchange - Reader account management - Shares and reader accounts - Data listing marketplace |
| Virtual Warehouses | 15% | - Warehouse creation and configuration - Cache concepts (warehouse, result, metadata) - Warehouse monitoring and troubleshooting - Warehouse sizing and scaling - Multi-cluster warehouses |
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NEW QUESTION # 656
Which of the following indicates that it may be appropriate to use a clustering key for a table? (Select TWO).
Answer: B,D
Explanation:
A clustering key in Snowflake is used to co-locate similar data within the same micro-partitions to improve query performance, especially for large tables where data is not naturally ordered or has become fragmented due to extensive DML operations. The appropriate use of a clustering key can lead to improved scan efficiency and better column compression, resulting in faster query execution times.
The indicators that it may be appropriate to use a clustering key for a table include:
D . Queries on the table are running slower than expected: This can happen when the data in the table is not well-clustered, leading to inefficient scans during query execution.
E . The clustering depth for the table is large: A large clustering depth indicates that the table's data is spread across many micro-partitions, which can degrade query performance as more data needs to be scanned.
References:
Snowflake Documentation on Clustering Keys & Clustered Tables
Snowflake Documentation on SYSTEM$CLUSTERING_INFORMATION
Stack Overflow discussion on cluster key selection in Snowflake
NEW QUESTION # 657
What is a recommended best practice when loading data into Snowflake?
Answer: B
Explanation:
To optimize parallelism and maximize performance, large files should be split into multiple smaller files (ideally 100-250 MB compressed) before loading. This allows Snowflake to load files in parallel using multiple threads or clusters.
Reference:
Snowflake Docs: Best Practices for Data Loading
NEW QUESTION # 658
How can a user validate if a micro-partition is pruning efficiently?
Answer: D
Explanation:
The correct answer is A. By inspecting the statistics pane in the Query Profile .
The Query Profile provides execution details for a query. To evaluate micro-partition pruning, users can inspect scan statistics, including partitions scanned versus total partitions. This helps determine whether Snowflake is efficiently skipping unnecessary micro-partitions.
Why A is correct:
The Query Profile statistics pane can show how many partitions were scanned and how many total partitions existed. Efficient pruning means Snowflake scanned only a small portion of the total relevant micro-partitions.
Example interpretation:
Partitions scanned: 100
Partitions total: 10,000
This indicates strong pruning because Snowflake skipped most micro-partitions.
Why the other options are incorrect:
B). Query details in Snowsight provide useful metadata such as duration, warehouse, and query text, but the Query Profile statistics pane is the best place to validate pruning efficiency.
C). SYSTEM$CLUSTERING_INFORMATION() gives clustering information for a table, such as clustering depth and overlap, but it does not directly show whether a specific query pruned efficiently.
D). INFORMATION_SCHEMA.TABLES provides table metadata, not query-level partition pruning details.
Official Snowflake documentation reference:
Snowflake documentation explains that Query Profile provides detailed execution information, including table scan statistics such as partitions scanned and partitions total. These statistics help validate pruning effectiveness.
Reference: Snowflake Documentation - Query Profile; Snowflake Documentation - Table scan statistics; Snowflake Documentation - Micro-partitions and pruning; SnowPro Core Study Guide - Performance Optimization.
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NEW QUESTION # 659
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 # 660
Which action can be performed by the SYSADMIN role without requiring elevated privileges from the ACCOUNTADMIN or SECURITYADMIN role?
Answer: C
Explanation:
The SYSADMIN system role is intended to manage Snowflake compute and database objects. By default, it can create virtual warehouses, databases, schemas, and other database objects, and it can manage objects it owns or has been granted privileges on. Creating users belongs to the USERADMIN role hierarchy, while broad privilege administration is handled by SECURITYADMIN through MANAGE GRANTS. Billing and account-usage administration normally require ACCOUNTADMIN or explicitly granted application or database roles and privileges. Therefore, managing virtual warehouses is the action that SYSADMIN can perform without first elevating to ACCOUNTADMIN or SECURITYADMIN. This separation supports least privilege by keeping object and compute administration separate from identity management, security-grant administration, and billing oversight.
NEW QUESTION # 661
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