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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake editions and features - Virtual warehouse concepts and credit usage - Snowflake architecture layers (storage, compute, services) |
| Topic 2: Virtual Warehouses | 15% | - Cache concepts (warehouse, result, metadata) - Multi-cluster warehouses - Warehouse sizing and scaling - Warehouse creation and configuration - Warehouse monitoring and troubleshooting |
| Topic 3: Account Objects | 8% | - Warehouses, databases, and schemas hierarchy - Resource monitors - Organizations and accounts structure |
| Topic 4: Time Travel & Cloning | 10% | - Time travel concepts and retention - Zero-copy cloning - Fail-safe concepts - UNDROP and historical data access |
| Topic 5: Performance Optimization & Monitoring | 4% | - Query profiling and performance tuning - Resource optimization techniques - Query history and monitoring |
| Topic 6: Data Sharing | 12% | - Private sharing vs. Data Exchange - Reader account management - Data listing marketplace - Shares and reader accounts |
| Topic 7: Security & Access Control | 10% | - Authorization (roles, privileges) - Row-level and column-level security - Masking policies - Governance best practices - Authentication (SSO, MFA) |
| Topic 8: Databases, Tables & Views | 18% | - Stored procedures and UDFs - Table types (permanent, transient, temporary) - Clustering and micro-partitions - Views (standard, materialized, secure) - Data manipulation (DML operations) - Column types and sequences |
| Topic 9: 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) |
| Topic 10: Semi-Structured Data | 10% | - VARIANT data type - Parsing and flattening semi-structured data - Array and object operations |
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NEW QUESTION # 41
Which Snowflake table supports unstructured data?
Answer: B
Explanation:
While Snowflake primarily deals with structured and semi-structured data, it also has the capability to handle unstructured data. Unstructured data can be stored in Snowflake using variants of SQL data types in tables, which can be permanent tables. These permanent tables, while traditionally used for structured or semi-structured data (like JSON, Avro, or Parquet), can also accommodate unstructured data in the form of binary formats or strings, offering flexibility in data storage and analysis. However, the management and querying of unstructured data in Snowflake may require additional considerations compared to structured data.References: Snowflake Documentation on Data Types
NEW QUESTION # 42
How does Snowflake utilize clustering information to improve query performance?
Answer: B
Explanation:
Snowflake utilizes clustering information to enhance query performance by pruning unnecessary micro-partitions.
Clustering Metadata: Snowflake stores clustering information for each micro-partition, which includes data range and distribution.
Pruning Micro-partitions: When a query is executed, Snowflake uses this clustering metadata to identify and eliminate micro-partitions that do not match the query criteria, thereby reducing the amount of data scanned and improving query performance.
References:
Snowflake Documentation on Clustering
Snowflake Documentation on Micro-partition Pruning
NEW QUESTION # 43
To switch between a Gen2 standard warehouse and a Snowpark-optimized warehouse, which warehouse object properties must be changed? Select TWO.
Answer: B,E
Explanation:
Snowflake warehouses can use different warehouse types and resource constraints. Snowpark-optimized warehouses use warehouse properties such as WAREHOUSE_TYPE and RESOURCE_CONSTRAINT to define the compute configuration.
Reference: Snowflake Documentation - Snowpark-optimized warehouses; CREATE WAREHOUSE; ALTER WAREHOUSE.
==
NEW QUESTION # 44
Using which feature or object will temporarily scale virtual warehouses running costly queries?
Answer: A
Explanation:
The correct answer is C. Query acceleration service .
The Query Acceleration Service is a Snowflake performance optimization feature that can temporarily provide additional compute resources to accelerate eligible queries. It is designed for queries that scan large amounts of data and would otherwise take longer or consume significant warehouse resources.
Why C is correct:
The Query Acceleration Service can offload portions of query processing to shared compute resources provided by Snowflake. This helps speed up expensive or long-running queries without permanently resizing the virtual warehouse.
Why the other options are incorrect:
A). Resource monitor controls and monitors credit usage. It can suspend warehouses or send alerts, but it does not scale compute for expensive queries.
B). Materialized view stores precomputed query results to improve performance for repeated query patterns, but it does not temporarily scale a warehouse.
D). Search optimization service improves point lookup and selective query performance on large tables, but it does not temporarily scale virtual warehouse compute.
Official Snowflake documentation reference:
Snowflake documentation describes the Query Acceleration Service as a feature that can improve the performance of eligible queries by using additional compute resources. It is commonly associated with accelerating parts of large scans and reducing the impact of outlier queries.
Reference: Snowflake Documentation - Query Acceleration Service; SnowPro Core Study Guide - Performance Optimization.
NEW QUESTION # 45
Which step will increase security related to user access to a Snowflake account?
Answer: D
Explanation:
The correct answer is B. Restrict access to Snowflake to known networks using a network policy .
A network policy improves account access security by allowing or blocking access based on IP address ranges. This helps ensure that users can connect to Snowflake only from approved networks.
Why B is correct:
Network policies can restrict inbound access to Snowflake by defining allowed and blocked IP address ranges. This is a direct security control for user access to a Snowflake account.
Example concept:
CREATE NETWORK POLICY corp_network_policy
ALLOWED_IP_LIST = ( ' 203.0.113.0/24 ' );
ALTER ACCOUNT SET NETWORK_POLICY = corp_network_policy;
Why the other options are incorrect:
A). A UDF is not the correct tool for managing account access security. Monitoring security parameters may provide visibility, but it does not directly restrict access.
C). A short session timeout can reduce risk from idle sessions, but making it "very short" can harm usability and is not as direct as network restrictions.
D). Every user already has access to the PUBLIC role by default. Setting restrictive permissions on PUBLIC is good practice, but it is not as direct as restricting access through a network policy.
Official Snowflake documentation reference:
Snowflake documentation describes network policies as security controls that restrict access to Snowflake based on allowed and blocked IP address lists.
Reference: Snowflake Documentation - Network policies; Snowflake Documentation - Network policy best practices; SnowPro Core Study Guide - Security and Access Control.
NEW QUESTION # 46
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