매달, 우리는 1000명 이상의 사람들이 시험 준비를 잘하고 시험을 잘 통과할 수 있도록 도와줍니다.
Snowflake Certified SnowPro Associate - Platform Certification 온라인 연습
최종 업데이트 시간: 2026년01월01일
당신은 온라인 연습 문제를 통해 Snowflake SOL-C01 시험지식에 대해 자신이 어떻게 알고 있는지 파악한 후 시험 참가 신청 여부를 결정할 수 있다.
시험을 100% 합격하고 시험 준비 시간을 35% 절약하기를 바라며 SOL-C01 덤프 (최신 실제 시험 문제)를 사용 선택하여 현재 최신 320개의 시험 문제와 답을 포함하십시오.
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Explanation:
Snowflake’s object hierarchy begins with the account, which contains one or more databases. Each database can contain multiple schemas, and each schema contains objects such as tables, views, stages, and functions. The schema is a logical boundary for grouping related database objects. A schema cannot contain databases, and tables cannot contain schemas. Instead, tables and views are contained within schemas. Therefore, the two correct hierarchical relationships are that a database can contain multiple schemas, and a schema can contain multiple tables and views.
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Explanation:
A Snowflake database is a top-level container that can include schemas, and within those schemas it can contain objects such astables, views, stages, functions, procedures, anddatabase roles. However, global account-level roles (such as SYSADMIN, SECURITYADMIN, PUBLIC, or custom account roles) do not reside inside a database. These roles are created and managed at the Snowflake account level, not within any specific database. In contrast, database rolesare scoped within a specific database and can exist inside it. Views and tables are standard database objects. Therefore, the only option that a Snowflake database cannot contain is an account-level Role.
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Explanation:
Snowflake supportsNon-Materialized Views (standard views), Materialized Views, and Secure Views. Non-materialized views compute results dynamically at query time. Materialized views store precomputed results, improving performance for repeated analytical queries. Secure views protect underlying query logic and metadata from being exposed, ensuring privacy when sharing datasets. The option “UnMaterialized” is not a Snowflake concept and does not exist as a view type.
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Explanation:
To create a new stage in Snowsight, users navigate through: Data → Database → Schema → Create Stage. Within the schema, selecting the "+" icon opens the option to create a stage. Users may configure internal or external stages, encryption settings, and directory table options. Other paths listed reference creating schemas rather than creating stages and are therefore incorrect.
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Explanation:
Snowsight’s Query History view allows filtering queries bystatus, date range, and executing user. Additional filters include SQL text search, warehouse, duration, statement type, session ID, query ID, query tag, parameterized query hash, and whether a query was generated by a client or task. These filters help users identify long-running, failed, queued, or specific types of queries for troubleshooting and performance optimization. Network or hardware configurations have no role in Query History filtering.
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Explanation:
Snowsight provides adrag-and-dropinterface for uploading data from a local machine into an internal stage. This is done via: Data → Add Data → Load Files into a Stage. Users can upload multiple files and choose the destination stage. This convenient UI eliminates the need for command-line tools such as SnowSQL. Snowsight supports both local file uploads and cloud storage references, not just cloud sources.
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Explanation:
Snowflake Notebooks provide native integration with Streamlit, allowing users to build interactive data apps directly inside Snowflake. Users can call Streamlit functions such as st.write(), st.dataframe(), st.button(), and st.slider() within Python cells. No external HTML, scripting, or network configuration is required. Streamlit components execute seamlessly in the notebook environment, enabling dashboards and interactive visualizations on top of Snowflake data.
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Explanation:
A red checkmark in Snowflake Notebooks indicates that the cell encountered anerrorduring execution. This visual indicator helps users quickly identify issues so they can inspect the error message and adjust code accordingly. Successful execution shows a green checkmark, while running cells show a spinner. Snowflake notebooks do not include a “paused” state for notebook cells.
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Explanation:
In Snowflake Python worksheets, settings include the HandlerandReturn Type. The Handler identifies the specific Python function that serves as the entry point for execution―essential when deploying code as stored procedures or user-defined functions. The Return Type defines how Snowflake interprets the output, such as STRING, VARIANT, or TABLE, ensuring proper result rendering. “Return Value” is not a worksheet setting but rather the outcome produced by the code itself, and “Output” is not a configuration option.
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Explanation:
Snowflake Notebooks provide an integrated, interactive development environment that enables users to write SQL and Python code, visualize results, and document analysis―all in the same workspace. They support data visualizations, charts, tables, and Streamlit-based interactions. Notebooks run entirely within Snowsight and require no manual hardware configuration. They are not CLI-based and support much richer displays than plain text. This integration streamlines the entire data exploration, transformation, and modeling workflow.
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Explanation:
Snowflake Notebooks support multiple cell types for development and documentation. Pythoncells allow users to perform data transformations, analysis, and Snowpark operations. SQLcells enable executing Snowflake SQL queries directly against tables and views. Markdowncells support formatted documentation within notebooks―useful for narrative explanations and commentary. The option “Markup” is not a supported cell type in Snowflake Notebooks. These three supported types allow users to blend analytics, code execution, and documentation in a unified environment.
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Explanation:
The official Snowflake web interface is called Snowsight. Snowsight is the modern, browser-based UI that enables SQL development, Python worksheets, dashboarding, visual exploration, and account monitoring. It replaces the classic UI and is now the default interface. Snowsight provides integrated capabilities such as notebooks, data loading interfaces, dashboards, role and warehouse management, and visual query history. Other options like Snowsite, Snow GUI, and Snow View are not valid Snowflake product names.
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Explanation:
Snowflake stores data in a compressed columnar format, which is optimal for analytical workloads. Columnar storage allows Snowflake to read only the relevant columns needed for query execution, improving performance significantly for aggregation and filtering operations. Compression further reduces storage consumption, enabling cost efficiency. This approach differs from row-store formats used in OLTP systems and provides better analytic processing. Snowflake automatically manages partitioning, micro-partitioning, and metadata without user intervention.
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Explanation:
Snowflake is a fully cloud-native platform that supports deployment across all three major cloud providers: AWS, GCP, and Azure. Snowflake delivers consistent functionality across each provider, allowing organizations to choose the cloud best suited for regulatory, architectural, and business requirements. Although small feature differences may exist between cloud providers, Snowflake’s core capabilities―virtual warehouses, storage layers, security, data sharing, and governance― operate uniformly across all three platforms. This multi-cloud support gives Snowflake strong flexibility for hybrid, multi-region, and multi-cloud deployments.
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Explanation:
Snowflake processes queries using Massively Parallel Processing (MPP)compute clusters, deployed as virtual warehouses. Each warehouse consists of multiple compute nodes working in parallel to execute queries efficiently. When a query is submitted, Snowflake distributes tasks across nodes, processes data subsets concurrently, and aggregates results. This architecture enables high performance, scalability, and the ability to handle complex analytical workloads. While Snowflake does incorporate elements of shared-disk storage, query execution itself depends on MPP compute clusters. Options such as third-party connectors or storage optimization do not represent the core query processing mechanism.