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

Snowflake’s multi-cluster shared data architecture enablesindependent scaling of compute and storage, meaning workloads can be sized differently without affecting stored data.

It also providesnear-unlimited concurrency, because Snowflake can automatically add compute clusters during peak loads.

Snowflake does not require manual infrastructure management and runs fully in the cloud—not on-premises. Multi-cloud support reduces vendor lock-in, not increases it.

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SHOW FILE FORMATS listsfile format objectsthat exist in the currently selected schema (or optionally, a specific database or schema). These objects define how Snowflake interprets staged files during data loading.

It doesnotlist supported built-in formats (CSV, Parquet, etc.).

It doesnotshow example usage information and doesnotlist files—LIST @stage handles that.

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Streamlit inside Snowflake Notebooks enables users to buildinteractive, real-time visualizations and data applicationsdirectly within Snowsight. Because Streamlit is event-driven, users can create UI components such as sliders, dropdowns, date pickers, buttons, and filters that dynamically update charts, tables, and computed outputs.

Data remains inside Snowflake, processed through Python with Snowpark, eliminating data movement to external systems. This allows rapid prototyping of dashboards, analytical workflows, and AI-driven apps. Streamlit also supports layout customization, reactive components, and seamless integration with Snowflake compute for scalable analytics.

Incorrect options:

Streamlit doesnotmanage data storage.

Streamlit does not simplify code execution; it enhances visualization and interactivity.

Warehouse clusters are not fixed-size—Snowflake warehouses can scale.

Thus, its primary benefit is enablingrich interactive analytics directly within Snowflake.

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Schemas serve as logical containers within a database, grouping tables, views, file formats, functions, and other objects.

Roles control access, not organization.

Tables store data but do not group objects.

Stages store files or point to external storage; they are not organizational containers.

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Snowflake Marketplace serves as a centralized catalog where providers publishready-to-use datasets, models, and applicationsacross industries. It includes:

Public datasets, such as demographics, weather, healthcare statistics, and government data.

Financial market datasets, including equities, commodities, macroeconomic indicators, and pricing feeds from major financial data vendors.

Third-party datasetssupplied by external organizations specializing in geospatial intelligence, retail analytics, marketing insights, healthcare, census data, and more.

“Proprietary datasets” are internal to organizations and typically not published on the public Marketplace unless the owner chooses to list them. Marketplace listings are curated and governed.

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The Snowsight object browser is Snowflake’s built-in visual interface for exploring and navigating all database objects within an account. It provides a hierarchical view across databases, schemas, and contained objects such as tables, views, stages, pipes, file formats, procedures, and functions. The key purpose of the object browser is to make it easy for users to view and filter database objects without needing SQL commands. Users can search objects by name, apply filters, preview table metadata, and inspect properties such as storage usage or row counts.

The object browser does not manage warehouses; warehouse management is done under the “Warehouses” section. It also does not create worksheets; worksheets are created in the “Worksheets” section. Additionally, the object browser does not modify security settings—permissions and grants are controlled through SQL (GRANT, REVOKE, SHOW GRANTS) or through the Access Control interfaces.

In summary, the object browser is a discovery and navigation layer, allowing users to efficiently find, inspect, and explore Snowflake objects.

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The Snowsight Monitoring tab provides a centralized view of virtual warehouse usage metrics, enabling administrators and developers to evaluate how compute resources are being consumed. This includes critical insights such as credit usage, query load, concurrency levels, average queue times, execution durations, and auto-scaling activity (for multi-cluster warehouses). These metrics help determine whether a warehouse is correctly sized, whether concurrency issues are occurring, or whether workloads require scaling up or adding clusters.

Query history is available in a different section—“Activity → Query History”—not under Monitoring. Time Travel snapshots are not visualized within Monitoring; Time Travel is controlled via retention parameters and accessed with SQL (AT/BEFORE clauses). Schema change history is also not part of Monitoring and instead is discoverable through ACCOUNT_USAGE or specific metadata views.

The Monitoring tab exists specifically to help evaluate warehouse performance and resource consumption, enabling optimization of compute spending and better workload management.

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SnowflakeCortex functionsenable AI-powered operations directly within SQL and Python, supporting multiple languages via translation, classification, summarization, and document extraction functions. They operate seamlessly onstructuredandsemi-structureddata stored in VARIANT columns, allowing LLMs to process text, JSON, documents, and rich content.

Cortex functions can be combined within SQL pipelines—for example, using PARSE_DOCUMENT to extract data, followed by CLASSIFY_TEXT to categorize extracted fields, and then SUMMARIZE to produce narrative summaries. This composability enables sophisticated multi-stage workflows for document intelligence, support ticket analysis, compliance automation, and semantic enrichment.

They arenot limited to a single domain; Cortex functions apply to finance, retail, support operations, healthcare, legal analysis, and general AI-assisted data processing.

Thus, Cortex functions are versatile, multi-domain, multi-language tools integrated with Snowflake’s compute and governance layers.

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Snowflake supports several numeric data types, includingINTEGER(and its aliases INT, BIGINT, SMALLINT, TINYINT) andFLOAT(a floating-point type). INTEGER represents exact whole numbers, and FLOAT supports approximate numerical values with fractional components. BOOLEAN is a logical data type used for TRUE/FALSE values, not numeric. VARCHAR is a string data type, also not numeric. Thus, INTEGER and FLOAT are the correct numeric data types supported by Snowflake.

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Every query executed in Snowflake is assigned a uniqueQuery ID, a system-generated identifier that allows users to retrieve, analyze, and reference the query. Query IDs support debugging, profile retrieval, audit tracking, workload analysis, and support cases.

Transaction IDs are only assigned to transactional operations and do not uniquely identify all queries.

Session IDs identify user sessions—not individual queries. Many queries can occur in the same session.

Account ID is the identifier for the entire Snowflake account and does not relate to specific queries.

Thus,Query IDis the unique attribute used to identify each query in Query History.

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