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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Virtual Warehouses | 15% | - Multi-cluster warehouses - Warehouse sizing and scaling - Cache concepts (warehouse, result, metadata) - Warehouse monitoring and troubleshooting - Warehouse creation and configuration |
| Topic 2: Data Sharing | 12% | - Shares and reader accounts - Data listing marketplace - Reader account management - Private sharing vs. Data Exchange |
| Topic 3: Databases, Tables & Views | 18% | - Clustering and micro-partitions - Stored procedures and UDFs - Views (standard, materialized, secure) - Data manipulation (DML operations) - Column types and sequences - Table types (permanent, transient, temporary) |
| Topic 4: Time Travel & Cloning | 10% | - UNDROP and historical data access - Zero-copy cloning - Time travel concepts and retention - Fail-safe concepts |
| Topic 5: Cloud Platform Overview & Snowflake Key Concepts | 8% | - Virtual warehouse concepts and credit usage - Snowflake architecture layers (storage, compute, services) - Snowflake editions and features |
| Topic 6: Performance Optimization & Monitoring | 4% | - Query history and monitoring - Query profiling and performance tuning - Resource optimization techniques |
| Topic 7: Data Loading & Unloading | 15% | - Continuous loading with Snowpipe - File formats (JSON, CSV, Parquet, AVRO) - Data unloading and staging - Bulk loading with COPY command - Data ingestion best practices |
| Topic 8: Semi-Structured Data | 10% | - Array and object operations - Parsing and flattening semi-structured data - VARIANT data type |
| Topic 9: Account Objects | 8% | - Organizations and accounts structure - Resource monitors - Warehouses, databases, and schemas hierarchy |
| Topic 10: Security & Access Control | 10% | - Masking policies - Authentication (SSO, MFA) - Authorization (roles, privileges) - Governance best practices - Row-level and column-level security |
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NEW QUESTION # 1066
A data provider shares a Snowflake secure view with a user who is outside of the provider ' s organization.
Which parameter can the provider use to impersonate the user and access the view?
Answer: A
NEW QUESTION # 1067
Which data types are supported by the Search Optimization Service? Select TWO.
Answer: D,E
Explanation:
The correct answers are A. INTEGER and B. GEOMETRY .
The Snowflake Search Optimization Service can improve performance for selective queries by maintaining a search access path. It supports several data types depending on the type of search being optimized, including scalar equality searches and geospatial searches.
Why A is correct:
INTEGER is a numeric data type. Search optimization supports equality searches on supported scalar data types, including numeric types such as integers.
Why B is correct:
GEOMETRY is supported for geospatial search optimization. Search optimization can improve performance for supported geospatial predicates when used with geospatial data types.
Why the other options are less appropriate in this question:
C). FLOAT is not the best answer in this option set. Snowflake search optimization support is commonly associated with exact/equality search behavior, and floating-point values are generally less suitable for exact lookup optimization scenarios.
D). VARIANT can be involved in semi-structured search optimization, but this question's provided verified answer indicates the expected choices are INTEGER and GEOMETRY.
E). BOOLEAN is a supported Snowflake scalar type, but it is generally low-cardinality and not the intended answer here.
Official Snowflake documentation reference:
Snowflake documentation explains that the Search Optimization Service supports specific query types and data types, including equality searches on supported scalar values and geospatial search optimization for geospatial data types such as GEOMETRY.
Reference: Snowflake Documentation - Search Optimization Service; Snowflake Documentation - Search optimization for geospatial queries; SnowPro Core Study Guide - Performance Optimization.
==
NEW QUESTION # 1068
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 # 1069
Which of the following are benefits of micro-partitioning? (Select TWO)
Answer: C,E
Explanation:
Micro-partitions in Snowflake are immutable objects, which means once they are written, they cannot be modified. This immutability supports the use of Time Travel, allowing users to access historical data within a defined period. Additionally, micro-partitions can significantly reduce the amount of I/O from object storage to virtual warehouses. This is because Snowflake's query optimizer can skip over micro-partitions that do not contain relevant data for a query, thus reducing the amount of data that needs to be scanned and transferred.
References: [COF-C02] SnowPro Core Certification Exam Study Guide
https://docs.snowflake.com/en/user-guide/tables-clustering-micropartitions.html
NEW QUESTION # 1070
What happens when an external or an internal stage is dropped? (Select TWO).
Answer: A,D
Explanation:
When an external stage is dropped in Snowflake, the reference to the external storage location is removed, but the actual files within the external storage (like Amazon S3, Google Cloud Storage, or Microsoft Azure) are not deleted. This means that the data remains intact in the external storage location, and only the stage object in Snowflake is removed.
On the other hand, when an internal stage is dropped, any files that were uploaded to the stage are deleted along with the stage itself. These files are not recoverable once the internal stage is dropped, as they are permanently removed from Snowflake's storage.
References:
[ COF-C03 ] SnowPro Core Certification Exam Study Guide
Snowflake Documentation on Stages
NEW QUESTION # 1071
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