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SnowflakeData Exchangeenables secure, governed sharing oflive databetween Snowflake accounts. It lets providers publish datasets to specific consumers—partners, vendors, departments, or customers—without copying or transferring data. Consumers query shared data instantly, leveraging Snowflake’s secure data sharing architecture.

Key capabilities include:

Targeted sharing to selected accounts

Fine-grained access management and auditing

Live, real-time data sharing without data movement

Ability to customize listings and visibility

Data Exchange is different from theMarketplace, which supports public or broad distribution.

It does not store raw data—storage occurs in provider accounts.

It does not manage user permissions (that is the role of RBAC).

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Snowflake compute resources are referred to asVirtual Warehouses. A virtual warehouse is a cluster of compute nodes that executes SQL queries, performs DML operations (INSERT/UPDATE/DELETE), and runs data loading or transformation tasks.

Virtual Warehouses provide:

Dedicated compute isolation

Independent scaling (resize at any time)

Concurrency support through multi-cluster mode

Auto-suspend and auto-resume for cost efficiency

While Virtual Warehouses consist of compute clusters under the hood, Snowflake abstracts the underlying VM and node architecture, exposing only the warehouse construct to users. This ensures simplicity and avoids operational burdens such as node management.

Incorrect terms like Data Nodes or Virtual Machines represent underlying infrastructure concepts not exposed to end users.

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COPY INTO <title> loads data from staged files (internal or external stages) into a Snowflake table.

It supports pattern matching, file formats, error-handling modes, and parallel loading.

It does not move files, does not load to a warehouse (warehouses only supply compute), and table-to-table copying uses INSERT INTO…SELECT.

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The FLATTEN table function expands semi-structured data such as arrays or objects stored in VARIANT columns. Its output includes several metadata columns, and the most important among them isVALUE, which contains the extracted element from the array or object. VALUE is the column that developers typically join on or reference to access the flattened data.

In addition to VALUE, FLATTEN returns KEY (for object keys), INDEX (position in arrays), SEQ (internal ordering), and PATH (hierarchical reference path). These other columns support analytical or transformation logic, but VALUE remains the primary output used for downstream SQL.

The other listed options—OUTPUT, ROWNUM, and LEVEL—are not part of the FLATTEN function’s output schema. ROWNUM and LEVEL may appear in other SQL contexts but not in FLATTEN results. Therefore, the correct answer is VALUE.

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COPY INTO <title> bulk-loads data from internal or external stages into Snowflake tables. It supports transformations, validation, and multiple file formats.

It does not replicate databases, manage roles, or create warehouses.

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Auto-scale enables Snowflake to dynamically start or stop clusters in a multi-cluster warehouse depending on workload pressure. When concurrency rises, Snowflake adds clusters; when demand decreases, it scales back down. This provides seamless performance during peak workloads and efficient resource usage during off-peak times. Auto-scale does not require manual action—it is fully automated once configured. It is not limited to standard warehouses; multi-cluster mode is available in Enterprise Edition and above. Ultimately, auto-scale ensures elasticity and cost-efficient compute allocation.

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Changing a Snowflake virtual warehouse from Medium to Large increases the compute resources allocated to that warehouse—such as CPU cores, memory, and I/O bandwidth. This process is calledscaling up, or vertical scaling. It is typically performed to improve performance for complex queries, large joins, or computationally heavy workloads.

Scaling down is the reverse—reducing warehouse size from Large to Medium or similar.

Scaling out refers to adding more clusters to a multi-cluster warehouse, enabling increased concurrency rather than increasing power per cluster.

Scaling in is the reverse of scaling out—reducing the number of active clusters.

Thus, increasing the size from Medium to Large is vertical scaling, also known as scaling up.

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The Snowflake multi-cluster shared data architecture separates compute and storage, enablingindependent scaling of each. Compute resources (virtual warehouses) operate independently from the centralized storage layer, allowing users to increase compute power without impacting storage costs—or vice versa. This flexibility provides major advantages for cost optimization and workload performance tuning.

The architecture also supportsnear-unlimited concurrency and elasticity. When many users or workloads run simultaneously, Snowflake can automatically add additional clusters to a multi-cluster warehouse, ensuring that no queries experience queuing or performance degradation. This capability is crucial for BI dashboards, ETL pipelines, and large organizations with varied usage patterns.

Columnar storage (option D) is indeed a Snowflake feature but is not unique to the multi-cluster architecture. Enhanced security arrives via Snowflake’s platform-wide mechanisms, not specifically because of multi-cluster architecture. Unstructured data loading (option B) is also not governed by multi-cluster behavior.

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Snowflake Notebooks provide an interactive environment where developers and analysts can combineSQL,Python, visualization libraries, and documentation. Use cases include exploratory analysis, ETL prototyping, data engineering workflows, machine learning model development, and dashboard creation using Streamlit components.

Notebooks allow mixing of SQL and Python cells with shared session state, enabling smooth transitions between Snowpark DataFrames, SQL queries, visual charts, and markdown explanations. They enhance reproducibility and collaboration, supporting versioning, parameterization, and seamless Snowflake compute integration.

Incorrect options:

Raw data is stored in Snowflake tables, not in notebooks.

User authentication is handled by the Cloud Services Layer.

Managing cloud storage is automatic and not a notebook responsibility.

Thus, Snowflake notebooks are an end-to-end development and analytics interface.

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VARIANT supports nested, flexible structures and enables querying via JSON path notation. It is optimized for semi-structured formats (JSON, Avro, ORC, Parquet).

STRING, VARCHAR, and TEXT store plain text and cannot represent nested structures without conversion.

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