SOL-C01 Exam Dumps - Snowflake SnowPro Advanced Certification Questions and Answers

The default Time Travel retention period for most objects in Snowflake is1 day(24 hours). Time Travel enables access to historical versions of data after updates, deletes, or drops. It also allows cloning databases, schemas, and tables at previous points in time, and recovering dropped objects.

While Snowflake Enterprise Edition and higher tiers allow retention periods up to 90 days, this extended window is not the default—administrators must explicitly configure it for each table, schema, or database.

Retention periods of 7, 45, or 90 days are possible only with higher service editions; the default for all accounts and objects remains 1 day unless explicitly overridden.

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A Snowflake database is alogical containergrouping one or more schemas. It does not store data physically in the traditional sense—Snowflake abstracts physical storage into its cloud-native micro-partitioned storage layer. Because schemas contain tables, views, and other objects, a database organizes schemas logically, enabling governance, naming conventions, and security boundaries.

A virtual warehouse is compute, not storage. A “collection of tables and views” better describes a schema, not a database. Snowflake separates logical structure from physical storage, so a database is not a physical file repository.

Thus, a database is best defined as a logical grouping of schemas.

Cortex LLM functions integrate large language model capabilities directly inside Snowflake. These functions enable:

• Sentiment analysis

• Summarization

• Document question answering

• Text classification

• Embedding generation for vector search

They operate directly on Snowflake data, require no external infrastructure, and support secure in-platform AI workloads.

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Snowflake’s architecture separates storage and compute, enabling:

• Compute scaling (up/down or multi-cluster) without changing storage

• Storage expansion without affecting compute

• Cost optimization by paying for compute only when needed

This separation does not impact governance, latency, or data loading requirements.

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Snowsight’sQuery Historyinterface provides comprehensive visibility into executed queries. Users can view:

Query text

Execution status (running, succeeded, failed)

Duration and start/end timestamps

Warehouse used and credits consumed

Partition pruning details

Error messages

Query profile for performance diagnostics

This allows developers, analysts, and administrators to troubleshoot slow queries, identify bottlenecks, and monitor compute usage.

Incorrect options:

Query History shows failed and running queries, not just successful ones.

Storage configuration is unrelated to query operations.

Network settings are managed by the Cloud Services Layer, not Query History.

Thus, Query History is a key operational tool in Snowsight.

Snowflake supports zero-copy cloning at thedatabase,schema, andtablelevels. Cloning creates a new metadata reference to existing data without physically duplicating micro-partitions, significantly reducing storage cost and clone creation time. Cloning adatabasereplicates all schemas and tables within it at that moment in time. Cloning aschemareplicates all objects inside that schema, including tables and views. Cloning atablecreates an isolated version of that table for development, testing, or analysis. Importantly,rolescannot be cloned because they are account-level security objects, not data objects. Privileges must be manually granted or recreated for cloned objects. Cloning enables rapid, efficient creation of consistent environments for workload isolation and testing.

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Standard permanent tables in Snowflake have a default1-day Time Travel retention period. During this period, users may query historical data, restore dropped tables, or clone past states. Higher editions allow extending this period up to 90 days. Temporary and transient tables have 0-day retention by default.

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CLASSIFY_TEXTis a Snowflake Cortex task-specific function designed to categorize free-form text into predefined buckets. It operates by applying LLM-driven classification logic based on category labels or descriptions provided by the user. A primary and practical use case isautomatically categorizing customer support ticketsbased on content, such as identifying billing issues, login problems, or product defects. This enables automated routing, prioritization, and operational analytics.

The function outputs structured results with the predicted label and confidence score. It is optimized for domain classification rather than open-ended text generation.

Incorrect options:

Generating SQL codeis handled by the COMPLETE function.

Translationis handled by TRANSLATE.

Extracting structured fields from documentsis the role of PARSE_DOCUMENT.

Thus, classification of incoming text streams is the natural use case of CLASSIFY_TEXT.

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The LIMIT clause limits the number of rows Snowflake returns from a query. It is commonly used to preview datasets, improve performance when exploring large tables, and paginate results. LIMIT may be combined with OFFSET to skip a number of rows. Sorting is performed with ORDER BY, filtering with WHERE, and grouping with GROUP BY—not with LIMIT. Therefore, restricting returned rows is the primary purpose of LIMIT.

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Snowflake supports unloading semi-structured data specifically intoJSONandParquetformats. JSON preserves semi-structured hierarchy and nested fields, making it ideal for interchange, backup, and integration with downstream systems. Parquet provides columnar, compressed, and metadata-rich storage well-suited for analytical reprocessing in data lakes or other systems. CSV is not semi-structured and cannot represent nested data accurately. XML, although a semi-structured format, is not supported by Snowflake as an unload format. When performing COPY INTO , users can specify FILE_FORMAT with TYPE=JSON or TYPE=PARQUET to export semi-structured content efficiently.

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