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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Semi-Structured Data | 10% | - Parsing and flattening semi-structured data - Array and object operations - VARIANT data type |
| Topic 2: Databases, Tables & Views | 18% | - Table types (permanent, transient, temporary) - Views (standard, materialized, secure) - Clustering and micro-partitions - Data manipulation (DML operations) - Stored procedures and UDFs - Column types and sequences |
| Topic 3: Cloud Platform Overview & Snowflake Key Concepts | 8% | - Virtual warehouse concepts and credit usage - Snowflake architecture layers (storage, compute, services) - Snowflake editions and features |
| Topic 4: Data Sharing | 12% | - Data listing marketplace - Shares and reader accounts - Private sharing vs. Data Exchange - Reader account management |
| Topic 5: Account Objects | 8% | - Resource monitors - Warehouses, databases, and schemas hierarchy - Organizations and accounts structure |
| Topic 6: Data Loading & Unloading | 15% | - Data unloading and staging - File formats (JSON, CSV, Parquet, AVRO) - Data ingestion best practices - Continuous loading with Snowpipe - Bulk loading with COPY command |
| Topic 7: Virtual Warehouses | 15% | - Multi-cluster warehouses - Warehouse sizing and scaling - Warehouse creation and configuration - Warehouse monitoring and troubleshooting - Cache concepts (warehouse, result, metadata) |
| Topic 8: Time Travel & Cloning | 10% | - Zero-copy cloning - UNDROP and historical data access - Fail-safe concepts - Time travel concepts and retention |
| Topic 9: Performance Optimization & Monitoring | 4% | - Resource optimization techniques - Query profiling and performance tuning - Query history and monitoring |
| Topic 10: Security & Access Control | 10% | - Row-level and column-level security - Governance best practices - Masking policies - Authentication (SSO, MFA) - Authorization (roles, privileges) |
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NEW QUESTION # 277
Which column data type is supported when defining a clustering key on a table?
Answer: C
Explanation:
The correct answer is A. VARCHAR .
A clustering key can be defined on supported table columns or expressions to help Snowflake organize micro- partitions and improve pruning for certain query patterns. Standard scalar data types such as VARCHAR are supported.
Why A is correct:
VARCHAR is a standard scalar data type and can be used in a clustering key.
Example:
CREATE TABLE customer_orders (
customer_id VARCHAR,
order_date DATE,
amount NUMBER
)
CLUSTER BY (customer_id);
Why the other options are incorrect:
B). VARIANT stores semi-structured data. Clustering directly on raw semi-structured columns is not the best answer here; clustering keys should use supported scalar columns or expressions.
C). OBJECT is a semi-structured data type and is not the correct choice for a clustering key column in this question.
D). GEOGRAPHY is not the correct supported clustering key data type in this context.
Official Snowflake documentation reference:
Snowflake documentation explains that clustering keys can be defined using table columns or expressions, and standard scalar columns such as string, number, and date/time columns are commonly used for clustering.
Reference: Snowflake Documentation - Clustering keys and clustered tables; Snowflake Documentation - Table data types; SnowPro Core Study Guide - Performance Optimization.
NEW QUESTION # 278
Which data types can be used in Snowflake to store semi-structured data? (Select TWO)
Answer: B,C
Explanation:
Snowflake supports the storage of semi-structured data using the ARRAY andVARIANT data types. The ARRAY data type can directly contain VARIANT, and thus indirectly contain any other data type, including itself. The VARIANT data type can store a value of any other type, including OBJECT and ARRAY, and is often used to represent semi-structured data formats like JSON, Avro, ORC, Parquet, or XML34.
References:[COF-C02] SnowPro Core Certification Exam Study Guide
NEW QUESTION # 279
Which feature of Snowflake's Continuous Data Protection (CDP) has associated costs?
Answer: B
Explanation:
Snowflake's Continuous Data Protection (CDP) features encompass several mechanisms designed to protect data and ensure its availability and recoverability. Among these features, the one that has associated costs is Fail-safe.
Fail-safe is an additional layer of protection that kicks in after the Time Travel period expires. While Time Travel allows users to access historical data within a defined retention period (which can vary from 1 to 90 days depending on the Snowflake edition), Fail-safe provides a further 7 days (for a total of 7 additional days beyond the Time Travel period) during which Snowflake retains the data. This period is primarily intended for Snowflake's internal operations to recover data in the event of extreme scenarios, such as significant operational failures, and is not directly accessible by customers for data recovery purposes.
The associated costs with Fail-safe arise because Snowflake continues to store the data beyond the customer-specified Time Travel period, thereby incurring additional storage costs. It's important to note that while users do not incur direct costs for enabling Fail-safe (as it is an automatic feature of Snowflake), the extended storage of data during this period contributes to overall storage costs.
References:
Snowflake Documentation on Continuous Data Protection: Continuous Data Protection (CDP) Snowflake Documentation on Fail-safe: Understanding Fail-safe
NEW QUESTION # 280
What is a key difference between the Snowflake CLI and SnowSQL?
Answer: A
Explanation:
The correct answer is B. Snowflake CLI is primarily used for managing workloads and applications, while SnowSQL is used for executing operations .
SnowSQL is Snowflake's command-line client that is primarily used to connect to Snowflake and execute SQL statements. It is commonly used for SQL operations, including running queries, loading data, unloading data, and executing scripts.
Snowflake CLI is a broader command-line tool designed for managing Snowflake workloads and application development workflows. It supports tasks related to Snowflake Native Apps, Snowpark, Streamlit, stages, tasks, and other development or deployment activities.
Why B is correct:
Snowflake CLI is more focused on managing Snowflake workloads, projects, and applications. SnowSQL is primarily used as a SQL command-line client to execute SQL operations against Snowflake.
Why the other options are incorrect:
A). This reverses the primary purposes of the tools. SnowSQL is not mainly for managing workloads and applications.
C). SnowSQL is not limited to DDL, and Snowflake CLI is not limited to DML.
D). Snowflake CLI is not limited to query execution, and SnowSQL is not a Python package management tool.
Official Snowflake documentation reference:
Snowflake documentation describes SnowSQL as the command-line client for connecting to Snowflake and executing SQL queries. Snowflake CLI is described as a command-line tool for developer workflows and managing Snowflake workloads and applications.
Reference: Snowflake Documentation - SnowSQL; Snowflake Documentation - Snowflake CLI; SnowPro Core Study Guide - Snowflake Account and Resource Management.
==
NEW QUESTION # 281
By definition, a secure view is exposed only to users with what privilege?
Answer: A
Explanation:
A secure view in Snowflake is exposed only to users with the OWNERSHIP privilege.This privilege ensures that only authorized users who own the view, or roles that include ownership, can access the secure view
NEW QUESTION # 282
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