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| Section | Weight | Objectives |
|---|---|---|
| Account Objects | 8% | - Warehouses, databases, and schemas hierarchy - Organizations and accounts structure - Resource monitors |
| Semi-Structured Data | 10% | - VARIANT data type - Array and object operations - Parsing and flattening semi-structured data |
| Time Travel & Cloning | 10% | - UNDROP and historical data access - Zero-copy cloning - Time travel concepts and retention - Fail-safe concepts |
| Databases, Tables & Views | 18% | - Column types and sequences - Stored procedures and UDFs - Views (standard, materialized, secure) - Table types (permanent, transient, temporary) - Data manipulation (DML operations) - Clustering and micro-partitions |
| Virtual Warehouses | 15% | - Warehouse monitoring and troubleshooting - Multi-cluster warehouses - Warehouse creation and configuration - Cache concepts (warehouse, result, metadata) - Warehouse sizing and scaling |
| Data Loading & Unloading | 15% | - Bulk loading with COPY command - File formats (JSON, CSV, Parquet, AVRO) - Data ingestion best practices - Continuous loading with Snowpipe - Data unloading and staging |
| Security & Access Control | 10% | - Authorization (roles, privileges) - Row-level and column-level security - Governance best practices - Masking policies - Authentication (SSO, MFA) |
| Data Sharing | 12% | - Shares and reader accounts - Data listing marketplace - Private sharing vs. Data Exchange - Reader account management |
| Cloud Platform Overview & Snowflake Key Concepts | 8% | - Snowflake editions and features - Snowflake architecture layers (storage, compute, services) - Virtual warehouse concepts and credit usage |
| Performance Optimization & Monitoring | 4% | - Resource optimization techniques - Query profiling and performance tuning - Query history and monitoring |
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NEW QUESTION # 113
Which data types does Snowflake support when querying semi-structured data? (Select TWO)
Answer: B,E
Explanation:
Snowflake supports querying semi-structured data using specific data types that are capable of handling the flexibility and structure of such data. The data types supported for this purpose are:
A). VARIANT: This is a universal data type that can store values of any other type, including structured and semi-structured types. It is particularly useful for handling JSON, Avro, ORC, Parquet, and XML data formats1.
B). ARRAY: An array is a list of elements that can be of any data type, including VARIANT, and is used to handle semi-structured data that is naturally represented as a list1.
These data types are part of Snowflake's built-in support for semi-structured data, allowing for the storage, querying, and analysis of data that does not fit into the traditional row-column format.
References:
Snowflake Documentation on Semi-Structured Data
[ COF-C03 ] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 114
Which metrics are provided in the Query Profile?
Answer: D
Explanation:
The Query Profile tool provides a visual breakdown of query execution including:
* Execution time per step
* Rows processed , scanned, or returned This helps analyze performance bottlenecks and optimize SQL.
Reference:
Snowflake Docs: Query Profile
NEW QUESTION # 115
A user is setting up Snowpipe automation to load data from an Amazon S3 bucket into Snowflake. The user wants Snowflake to automatically start loading files as soon as new ones are added to the bucket. Which steps are required to configure this automation using cloud messaging? Select TWO.
Answer: B,C
Explanation:
For Snowpipe auto-ingest on Amazon S3, cloud event notifications are configured so Snowflake is notified when new files arrive. The pipe must also be created with AUTO_INGEST = TRUE.
Why A and D are correct:
Amazon S3 event notifications can publish file arrival events through Amazon SNS/SQS integration, and Snowpipe uses those notifications when AUTO_INGEST is enabled.
Reference: Snowflake Documentation - Snowpipe auto-ingest on Amazon S3; CREATE PIPE.
NEW QUESTION # 116
What is a characteristic of materialized views in Snowflake?
Answer: A
Explanation:
One of the characteristics of materialized views in Snowflake is that they allow multiple tables to be joined in the underlying query.This enables the pre-computation of complex queries involving joins, which can significantly improve the performance of subsequent queries that access the materialized view4.References:[COF-C02] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 117
Which of the following compute resources or features are managed by Snowflake? (Select TWO).
Answer: B,E
Explanation:
Snowflake manages various compute resources and features, including Snowpipe and the ability to scale up a warehouse. Snowpipe is Snowflake's continuous data ingestion service that allows users to load data as soon as it becomes available. Scaling up a warehouse refers to increasing the compute resources allocated to a virtual warehouse to handle larger workloads or improve performance.
References:
[COF-C02] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Snowpipe and Virtual Warehouses1
NEW QUESTION # 118
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