Free and Premium Snowflake SOL-C01 Dumps Questions Answers

When a LIMIT clause is applied without an ORDER BY, Snowflake does not guarantee which rows will be returned. This means the result of:

SELECT count FROM my_table LIMIT 10;

is anon-deterministic set of 10 rows. Because Snowflake stores data in micro-partitions and distributes processing across compute resources, the natural order of rows is undefined unless explicitly controlled.

To retrieve the lowest or highest values, users must specify ORDER BY count ASC or ORDER BY count DESC respectively. Without ordering, Snowflake simply returnsany10 qualifying rows.

• Option B (highest values) requires ORDER BY count DESC.

• Option D (lowest values) requires ORDER BY count ASC.

• Option C (values ≤ 10) requires a WHERE clause (WHERE count <= 10).

Therefore, the query returns a non-predictable sample of 10 rows from the column, making option A correct.

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COPY INTO

When auto-suspend is enabled on a virtual warehouse, Snowflake automatically suspends (stops) the warehouse after it has been idle for the configured period of time. Once suspended, the warehouse stops consuming compute credits, thereby reducing costs, since Snowflake bills only for active compute usage. When new queries arrive, auto-resume (if enabled) restarts the warehouse automatically.

Auto-suspend does not change the warehouse size; scaling up or down is a separate configuration. The warehouse is not dropped—its metadata and settings remain intact. Costs may be reduced and made more efficient but are not guaranteed to “stabilize month-to-month,” as the bill still depends on actual usage and workload patterns.

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Snowflake allows users to list files in a stage using the LIST command, and it provides a shorthand alias: LS. Both commands return identical results, including file name, size, last modified timestamp, and other metadata stored in the stage. This is relevant for internal stages (user, table, named stages) and external stages backed by cloud storage. SHOW and DESC are unrelated commands—SHOW lists Snowflake metadata objects such as tables, schemas, or roles, while DESC describes object structures. “LIS” is not a Snowflake command. Therefore, LS is the exact shorthand synonym for LIST and is used identically in queries.

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Amulti-cluster warehouseprovides Snowflake’s highest level of concurrency support by allocating multiple compute clusters behind a single warehouse definition. It automatically adds clusters during peak workload to eliminate query queuing and maintain stable performance for dashboards, BI tools, and multi-user environments.

Improved concurrencyis the primary benefit—large numbers of simultaneous queries are distributed across clusters.

Automatic scaling(scaling out and in) ensures compute cost efficiency, as Snowflake adds clusters only when needed and removes them when load decreases.

Incorrect options:

Storage costs are unaffected because compute and storage are independent.

Faster data loading may occur if warehouse size increases, but multi-cluster design is driven by concurrency, not ingestion performance.

The design enables smooth operation in high-demand enterprise environments.

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Data listings in the Snowflake Marketplace can be offered asfree or paiddepending on how the provider chooses to distribute the data. Providers can use usage-based pricing, subscription models, or make datasets available at no cost. Listings are not restricted to static data; Snowflake supportslive data sharing, enabling instant access to continuously updated datasets without copying or ETL. Listings are accessible to any Snowflake user with the appropriate marketplace access—not exclusively administrators. This flexibility makes the Marketplace a powerful ecosystem where organizations can seamlessly obtain external datasets for analytics, machine learning, and reporting needs. Providers define terms of use, pricing, and refresh schedules, while Snowflake manages the secure data-sharing infrastructure.

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The central difference between aStandard Warehouseand aMulti-cluster Warehouselies inhorizontal scalability and concurrency handling. A Standard Warehouse consists of one compute cluster. Though it can scaleup or down vertically(e.g., Small → Medium), it cannot automatically add more clusters during periods of heavy workload. This means that under peak concurrency, queries can queue until compute resources are available.

AMulti-cluster Warehouse, by contrast, supportsautomatic horizontal scaling, allowing Snowflake to add or remove clusters dynamically based on query demand. This ensures consistent performance under simultaneous workloads, making it essential for high-concurrency analytical environments or BI dashboards.

Both warehouse types share characteristics such as auto-resume, auto-suspend, and compatibility with all query workloads. They differ only in concurrency scaling. Multi-cluster warehouses can still auto-suspend at the warehouse level; the belief they cannot is incorrect. Standard Warehouses are fully functional for all workloads—they are not restricted to loading tasks.

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TheCOPY INTOcommand is Snowflake’s primary bulk-loading mechanism for ingesting data from stages (internal or external) into tables. It can read from Amazon S3, Azure Blob Storage, Google Cloud Storage, or internal Snowflake stages. COPY INTO supports file formats (CSV, JSON, Parquet), pattern matching, error handling, transformations, and validation modes.

PUT uploads filesfrom a local machineto an internal stage—not from stage to table.

GET downloads files from an internal stage to a local system.

INSERT INTO is for row-by-row inserts and is not designed for large file ingestion.

Therefore, COPY INTO is the correct and recommended method for loading stage files into Snowflake tables.

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Snowflake Cortex AI provides built-in AI functions and tools designed to work natively with unstructured and structured data. Two key capabilities are:

• Extract and classify text using functions like PARSE_DOCUMENT, EXTRACT_TEXT, and classification models. Cortex can process documents, identify relevant fields, and convert unstructured content into usable structured formats.

• Simplify unstructured data workflows by combining document extraction, vector search, summarization, and AI reasoning tools (e.g., Cortex Analyst, Cortex Search) directly inside Snowflake without external services.

It does not provide Marketplace data sharing features, which belong to Snowflake’s Data Sharing platform. Loading semi-structured data is a core Snowflake capability using VARIANT and COPY INTO—not Cortex-specific. Enhancing data security is a platform-wide feature, not a Cortex function.

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Aprivate listingis shared only with explicitly selected Snowflake accounts, giving providers fine-grained control over who can access their data. This is ideal for partner organizations, internal business units, or customers requiring restricted access. Apublic listing, on the other hand, is accessible to all Snowflake users via the Marketplace, making it available for broad distribution. Pricing models for both types can be free or paid; neither category inherently requires payment. Both listing types share data, not code. Public listings are not restricted to Snowflake employees—they are open to the entire Snowflake ecosystem. The distinction lies entirely in access scope: targeted (private) vs. universal (public).

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In Snowflake Notebooks, agreen checkmarkis a visual indicator that the cell hassuccessfully executed, meaning the computation completed without errors and results are available. This helps users verify execution state quickly across multi-step workflows.

Other visual states include:

A spinning icon for cells that are currently executing

A red error symbol for failed executions

Plain, unmarked cells that have not yet run

Snowflake Notebooks support SQL and Python cells with integrated compute, output visualization, and cell-level execution state tracking. There is no “paused” state for notebook cells.

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Snowflake Notebooks currently support three primary cell types: SQL, Python, and Markdown. SQL cells allow users to execute SQL queries directly against Snowflake data. Python cells enable computation, data transformation, machine learning, and visualization using Snowpark, pandas-like APIs, and Python libraries. Markdown cells provide rich text formatting to document workflows, add explanations, and create readable narratives within the notebook.

Languages such as Java, Scala, and R are supported by Snowflake outside notebooks—for example, through Snowpark APIs or external integrations—but they cannot be used directly as Notebook cell types. Notebooks are designed to integrate SQL and Python seamlessly while providing a documentation layer, making SQL, Python, and Markdown the correct and only supported options.

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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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The correct SQL command for removing a schema in Snowflake is:

DROP SCHEMA temp_schema;

This command deletes the schema and all objects contained within it, including tables, views, stages, file formats, and sequences. Snowflake performs this operation atomically, ensuring metadata consistency during the drop process. Users can also includeIF EXISTSor theCASCADEkeyword to handle dependencies more explicitly:

DROP SCHEMA IF EXISTS temp_schema CASCADE;

This safely handles scenarios where the schema may not exist or contains objects that would normally block deletion.

Incorrect options:

DROP DATABASE temp_schemaremoves an entire database, not a schema.

DELETE SCHEMAis not valid SQL; SQL uses DROP for schema removal.

DROP VIEW temp_schemaapplies only to removing a view object.

Dropping a schema requires USAGE and OWNERSHIP privileges, typically granted to roles such as SYSADMIN or ACCOUNTADMIN.

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Snowsight’s file-loading interface:

•Automatically detects data typesby inspecting column values.

•Loads (inserts) file contents directly into a table, either a new table or an existing one.

Snowsight does not automatically create permanent file formats, does not merge rows, and does not optimize warehouse-level performance.

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Snowflake’s file format options allow granular control over CSV ingestion. The SKIP_HEADER = 1 parameter instructs Snowflake to ignore the first line of the file, typically containing column headers. This is the correct and official parameter within FILE FORMAT objects or inline COPY INTO options. Other options listed—such as SKIP_FILE_HEADER, IGNORE_HEADER, or HEADER_SKIP—are not valid Snowflake parameters. When SKIP_HEADER is applied, Snowflake begins reading data rows starting from line 2. This ensures proper alignment between file contents and table structure during ingestion. SKIP_HEADER can also be set to values greater than 1 if multiple header rows exist. This behavior is essential when loading structured data where the header row is not intended for table consumption.

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Internal stages are fullySnowflake-managed storage locationsused to temporarily store data files for loading or unloading operations. Examples include:

User stages (@~)

Table stages (@%table_name)

Named internal stages (@my_internal_stage)

Because they resideinside Snowflake, users do not need AWS, Azure, or GCP credentials to access them. Internal stages support all file types Snowflake can process, including CSV, JSON, Parquet, Avro, ORC, images, PDFs, and other semi-structured formats.

Incorrect statements:

They arenot limitedto structured or semi-structured data—they accept all supported formats.

External cloud credentials are only needed for external stages (S3, Blob, GCS).

Internal stages simplify secure data ingestion and operational pipelines because Snowflake handles encryption, metadata, and lifecycle management.

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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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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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LIST @stage_name displays all files present in a stage (internal or external).

PUT uploads local files to an internal stage.

GET downloads files from an internal stage.

COPY INTO loads or unloads data but does not list files.

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Executing anINSERTcommand in a Snowflake worksheet requires three components to be explicitly selected:

Active Warehouse– Provides compute resources to process the query. Without an active warehouse, DML operations cannot execute.

Database– The table receiving the INSERT must reside in the active database context.

Schema– The targeted table must be within a selected schema.

All three define the fully qualified context for locating and writing to the correct object. Snowflake’s compute/storage separation ensures compute is only used when warehouses are active. Schema and database selection ensure correct namespace resolution.

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TheCOMPLETEfunction is the only Cortex LLM function where the user can explicitly specify the model name (e.g., Mistral, Snowflake Arctic, Meta Llama models).

SUMMARIZE, SENTIMENT, and TRANSLATE are task-specific functions that use built-in, preselected models anddo not allowmodel selection.

The Snowsight Query Profile is a powerful diagnostic tool that provides a visual breakdown of how Snowflake executed a query. Its primary purpose is to help users visualize and analyze query performance. It displays execution steps, including scan operations, join strategies, pruning results, aggregation methods, and data movement between processing nodes.

The profile shows metrics such as execution time per step, partition pruning effectiveness, bytes scanned, and operator relationships. This allows developers, analysts, and DBAs to identify bottlenecks—such as unnecessary full-table scans, non-selective filters, or inefficient joins—and tune SQL accordingly.

Query Profile does not execute queries; execution happens in worksheets or programmatic interfaces. It does not create objects or manage data loading; those tasks involve separate SQL commands and UI interfaces.

Overall, Query Profile is essential for performance tuning, helping teams reduce compute costs, optimize warehouse sizing, and improve query efficiency.

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TheACCOUNTADMINrole is Snowflake’s highest-privileged system-defined role. It provides complete administrative authority across the entire Snowflake account. Its functions include:

Managing global account parameters, replication settings, business continuity, failover groups, and region configurations

Administering billing, resource monitoring, and governance

Granting and revoking privileges across all objects and roles

Overseeing role hierarchy, including SECURITYADMIN and SYSADMIN

It is typically reserved for platform owners and security/governance teams, following least-privilege principles.

“Create and manage databases” is primarily a SYSADMIN responsibility.

“Monitor query performance” can be accomplished by roles with MONITOR privileges; it is not exclusive to ACCOUNTADMIN.

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Role hierarchy in Snowflake allows one role to inherit the privileges of another. By granting roles to other roles, Snowflake enables scalable, maintainable access control. Higher-level roles grant privileges downward, allowing administrators to create layered access structures. This hierarchy simplifies permission management across teams and environments. It has no relation to SQL sequencing, network settings, or data storage.

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TheGRANTcommand is used in Snowflake to assign privileges to a role. GRANT allows administrators to authorize roles to perform actions on objects such as tables, warehouses, schemas, or databases. The REVOKE command removes privileges, CREATE creates objects, and ASSIGN is not a valid Snowflake command. Therefore, GRANT is the correct command for privilege assignment.

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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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The COPY INTO

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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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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Snowflake uses the SQL command CREATE WAREHOUSE warehouse_name to create a new virtual warehouse. Warehouses provide the compute resources for executing SQL queries, loading data, and performing transformations. When creating a warehouse, additional parameters may be specified, such as warehouse size, auto-suspend timeout, auto-resume, scaling policy, or multi-cluster mode. The options involving “CREATE VIRTUALWAREHOUSE” are invalid syntax in Snowflake. CREATE DATABASE and CREATE SCHEMA create storage containers, not compute resources. Therefore, CREATE WAREHOUSE is the only correct command for provisioning compute.

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ADatabaseis the highest logical container for schemas and their objects in Snowflake.

Hierarchy:

Account → Database → Schema → Tables, Views, Stages, etc.

Schemas organize objects within a database; tables and views do not contain objects.

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A single VARIANT value in Snowflake has a maximum size limit of16 MB(uncompressed). This limit applies to semi-structured data stored in VARIANT, such as JSON, XML, Avro, and Parquet objects.

This limit ensures efficient storage, query performance, and micro-partitioning. Exceeding the size results in load or processing errors. For ingestion, when loading large JSON files, it’s recommended to break them into multiple objects or use JSON Lines formatting.

Snowflake has introduced an optional behavior change bundle (2025_03), increasing the limit to128 MBfor accounts that enable it, but the standard default remains 16 MB.

Thus, under default behavior,16MBis the correct maximum value size.

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ThePARSE_JSON()function converts a valid JSON string into aVARIANTvalue. This allows Snowflake to store and query nested, hierarchical data using dot and bracket notation.

Example:

SELECT PARSE_JSON('{"name":"John","age":30}') AS data;

After parsing, fields can be accessed like:

data:name or data['age']

Incorrect options:

TO_JSON()converts VARIANT to a JSON string (opposite direction).

CONVERT_JSONandEXTRACT_JSONare not Snowflake functions.

PARSE_JSON is essential for dynamically loading, transforming, or analyzing JSON content from files, streams, or external applications.

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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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AUTO_SUSPEND minimizes cost by suspending compute after a specified idle period.

It prevents unnecessary credit consumption while ensuring compute resumes automatically when needed.

Performance improvements depend on warehouse size, not suspension settings.

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Snowflake supports several native data types for managing temporal information:

DATE – Stores dates only (year, month, day) without any time component.

TIMESTAMP – Represents date and time values, including several variants:

• TIMESTAMP_NTZ (no time zone)

• TIMESTAMP_LTZ (local time zone)

• TIMESTAMP_TZ (explicit time zone)

DATETIME – An alias for TIMESTAMP_NTZ, storing both date and time.

These data types enable powerful time-based analytics and comparisons.

❌TIMEDATE is not a valid data type.

❌Python and JavaScript are programming languages, not Snowflake data types.

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Snowflake Virtual Warehouses support several configuration parameters that directly influence compute behavior, performance, and cost control.Auto-suspend timedetermines how long the warehouse should remain idle before Snowflake automatically suspends it to save credits.Auto-resumeenables automatic warehouse reactivation whenever a new query is submitted, ensuring seamless user experience without manual intervention.Warehouse sizedetermines the compute resources available (e.g., X-SMALL, SMALL, MEDIUM, LARGE). Larger warehouses provide more CPU, memory, and parallel processing ability. Conversely, thecloud provider regioncannot be configured at the warehouse level; it is determined when the Snowflake account is created and applies globally across the account. These warehouse settings enable efficient workload management, dynamic compute scaling, and cost optimization, allowing Snowflake users to tailor compute behavior to their analytics and data processing needs.

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GET_PRESIGNED_URL() is the official Snowflake function used to create atemporary, secure, time-limited pre-signed URL, which provides controlled-access retrieval of a file stored in a Snowflake internal or external stage. Pre-signed URLs are essential for secure file sharing without exposing credentials or granting direct stage access to external users. The function accepts the stage name and file path and optionally allows specifying an expiration period. GET_STAGE_LOCATION() retrieves metadata about where a stage points, not access URLs. BUILD_STAGE_FILE_URL() constructs a basic URL but does not sign it, meaning it cannot authorize secure download access. GET_RELATIVE_PATH() provides path-only details, not a secure download link. GET_PRESIGNED_URL ensures that external consumers can access staged files safely, making it an important feature for secure distribution workflows, data export pipelines, partner integrations, and temporary file sharing scenarios.

Snowflake provides multipledate and time data typesto handle various temporal workloads. TheDATEtype stores only the calendar date (year, month, day), suitable for dimensional modeling, slowly changing dimensions, and calendar-related analytics.

TIMESTAMPrepresents a point in time and includes sub-second precision. Snowflake supports multiple variants:

TIMESTAMP_NTZ(no time zone)

TIMESTAMP_LTZ(local time zone)

TIMESTAMP_TZ(explicit time zone)

These options allow flexibility for global operations, event logs, and time-series analytics.

DATETIMEis an alias forTIMESTAMP_NTZ, meaning it stores both date and time but without time-zone awareness. It is commonly used in ETL, application logs, and system-generated events.

TIMEDATEis not a valid Snowflake data type and does not exist in Snowflake’s type system.

These temporal types support extensive built-in datetime functions, automatic casting, and integration with semi-structured data through VARIANT.

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Snowflake Data Exchange is not enabled by default. Accounts must open a support request to Snowflake Support, which then activates the feature. It cannot be enabled via UI or SQL. After activation, administrators may create private exchanges and listings.

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The correct SQL syntax to create a zero-copy clone of an existing table is:

CREATE TABLE MY_TABLE_CLONE CLONE MY_TABLE;

This command instantly creates a new table that references the same underlying micro-partitions as the original. Because of Snowflake’smetadata-only cloning, no storage is consumed at the time of creation. Storage only increases when either the original or the clone diverges through DML operations, following acopy-on-writemodel.

Cloning is available for multiple object types—tables, schemas, databases, stages, streams, tasks, and more. This capability enables rapid creation of development sandboxes, QA environments, rollback copies, or controlled experimentation without duplicating data.

Incorrect options:

“COPY TABLE” is not a valid Snowflake command.

BACKUP/RESTORE are not Snowflake SQL commands.

RESTORE applies only to Time Travel or Fail-safe, not to cloning.

Thus, the CLONE keyword is the only correct method for zero-copy duplication.

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The Cloud Services Layer is the coordination and control layer of Snowflake’s architecture. One of its primary responsibilities is managing security, metadata, authentication, and system-wide services. This layer handles user authentication, role-based access control, metadata services (such as table structures, micro-partition metadata, statistics), query parsing, optimization, execution coordination, and transaction management.

It does not store customer data; storage is handled by the Database Storage Layer using micro-partitions. It does not manage virtual warehouses directly; warehouses are part of the Compute Layer. While Snowsight is a UI that interacts with the Cloud Services Layer, the interface itself is not part of the architectural layer.

The Cloud Services Layer essentially acts as the “brain” of Snowflake, ensuring the platform is consistent, secure, optimized, and able to scale operations intelligently across compute clusters and cloud-native storage environments.

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An external stage is the Snowflake object that encapsulates a secure connection to external cloud storage such as Amazon S3, Azure Blob Storage, or Google Cloud Storage. It stores the location (URL or bucket path) and, where required, credentials or role-based access configuration, and may also reference a file format. External stages are used as the source or target for COPY INTO operations when loading from or unloading to external storage.

An external table provides a logical SQL interface to data stored externally but relies on a stage for connectivity; it does not itself define the connection. A directory table exposes metadata about files stored in a stage, not the connection. A named file format defines parsing rules (type, delimiter, compression) but has no knowledge of or connection to a specific external storage location.

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Snowflake supports bothTOPandLIMITas valid SQL mechanisms for restricting the number of rows returned by a query. They function similarly but are used in different syntactic positions.

LIMITappears at the end of the query:

SELECT * FROM my_table LIMIT 10;

This tells Snowflake to return only the specified number of rows.

TOPis used in the SELECT clause:

SELECT TOP 10 * FROM my_table;

Both approaches are equivalent in functionality and are supported to maintain compatibility with ANSI SQL and other SQL dialects.

The other options are not correct row-limiting mechanisms:

•SELECTis a required keyword but does not limit rows on its own.

•GETis not a SQL keyword for row restriction; GET is used for downloading files from internal stages.

•ROW_NUMBERis a window function used for ranking rows, not constraining row count.

Therefore, TOP and LIMIT are the only correct SQL keywords in Snowflake for controlling how many rows are returned by a query.

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Snowflake’s Python Worksheets require theSnowpark for Pythonpackage. Snowpark integrates deeply with Snowflake’s compute engine, enabling DataFrame operations, UDF creation, ML workflows, and direct execution of Python logic inside Snowflake compute.

While additional libraries like Pandas, NumPy, and Matplotlib may be available in worksheets, they arenot requiredto enable the Python execution environment. Snowpark is the core computational interface that connects Python to Snowflake’s data and compute layers.

It enables:

Distributed data processing

Transformations expressed in Python but executed inside Snowflake

Access to Snowflake tables as DataFrames

Integration with Snowpark ML and Cortex functions

Thus, Snowpark is the essential package for Python worksheet support.

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Snowsight provides an intuitive UI-based workflow for loading data, enabling drag-and-drop file upload, schema inference, previewing records, automatic table creation, and loading into existing tables. Users can load CSV, JSON, Parquet, AVRO, ORC, and other supported formats without writing COPY INTO commands manually.

Snowsight also generates the equivalent SQL code, helping users understand and reuse ingestion scripts in pipelines. It eliminates the need for external tools such as SnowSQL CLI or custom ETL scripts.

Incorrect statements:

External tools are not required.

Manual storage configuration is unnecessary; Snowflake abstracts all storage.

Snowsight supports a wide variety of formats, not limited ones.

Thus, the key advantage is asimple, guided data-loading workflow, especially useful for new Snowflake users.

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Snowflake processes queries usingMassively 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.

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In Snowflake Notebooks, Python and SQL operate in an integrated environment. Python variable substitution is performed usingstring placeholders or f-string interpolationwithin SQL statements. This allows dynamic query construction where Python values are embedded directly into SQL code executed through the Snowpark session.

Example:

table_name = "CUSTOMERS"

session.sql(f"SELECT * FROM {table_name}").show()

This method enables parameterized, context-aware SQL generation—ideal for iterative development, automation, and pipeline construction. It avoids manual text editing and provides consistency across Python and SQL execution layers.

Incorrect responses:

Network settings and HTML are unrelated to variable handling.

Editing data files does not apply to dynamic query parameterization.

Variable substitution is essential for combining logic, iteration, and conditional flows within notebooks.

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As a fully managed cloud data platform, Snowflake is responsible for infrastructure provisioning, hardware, software installation, platform upgrades, scaling, and internal metadata management such as micro-partitions and statistics. Customers do not manage physical hardware or install Snowflake software.

Customers are responsible for their data and its lifecycle within Snowflake. This includes loading data into tables from internal and external sources, unloading data when required, organizing data structures (databases, schemas, tables), defining access controls, and managing how data is used, transformed, and governed. They design schemas and workloads but do not manage the underlying engine. Therefore, “Loading, unloading, and managing data” correctly describes the customer’s responsibility.

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In Snowflake’s Secure Data Sharing model, the accountsharingdata is known as thedata provider. Providers create shares that include selected objects (tables, views, secure views, etc.) and grant access to consumer accounts.

Adata consumerreceives the shared data.Data readeranddata administratorare not defined terms in Snowflake’s sharing architecture.

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Snowflake notebooks use the.ipynbfile extension, the standard format for Jupyter notebooks. This format stores executable code, markdown, metadata, and cell outputs in a structured JSON layout. Snowflake adopts this format to ensure compatibility with the broader Python ecosystem, thereby enabling seamless migration between Snowflake and external notebook environments.

The .ipynb structure allows mixed SQL and Python cells, visualizations, Streamlit components, documentation, and stepwise development within Snowsight. It supports reproducibility, collaboration, and integration with Snowpark and Cortex.

Incorrect formats:

.ipnbis a misspelling and invalid.

.sqlis used for SQL scripts only.

.txtcannot represent notebook metadata or cell structure.

Thus, .ipynb is the correct and only supported notebook format.

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The official Snowflake web interface is calledSnowsight. 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, SnowGUI, and SnowView are not valid Snowflake product names.

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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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CREATE STAGE is the correct Snowflake DDL command for creating a named internal stage. Stages hold files for loading/unloading. The command defaults to internal storage unless external parameters are provided.

Other options are not valid Snowflake syntax.

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In Snowflake’s logical hierarchy, tables reside within schemas, and schemas reside within databases. This structured arrangement allows clear organization, access management, and separation of workloads or business domains. A schema functions as a container that holds tables, views, functions, sequences, stages, and file formats. When users reference a table, they typically qualify it using the full three-part name: database.schema.table.

Stages are used to store or reference files, not to hold structured database tables. Virtual warehouses provide compute power for executing queries and performing DML operations but do not store data. File formats define how staged files should be interpreted (CSV, JSON, Parquet, etc.) and are unrelated to where tables physically reside.

Snowflake separates compute and storage, so while compute happens in virtual warehouses, all persistent data—including tables—is managed in Snowflake’s centralized storage layer, organized through schemas and databases. This organization ensures consistency, ease of management, and predictable access control.

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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.

TheData Storage Layerin Snowflake stores all data in acompressed, encrypted, columnar formatdesigned for analytical efficiency. Snowflake automatically reorganizes ingested data into micro-partitions optimized for pruning and high-performance scans. The compression and structuring are fully automated, requiring no tuning or indexing by the user.

Another foundational feature iselastic storage, made possible by Snowflake’s cloud-native architecture. Storage automatically scales up or down based on data volume without user intervention, and is separated from compute. This ensures cost efficiency and immediate availability of capacity for large datasets.

Incorrect options:

In-memory storageis not a Snowflake feature; long-term data is persisted in cloud object storage.

Elastic Cloudis not a Snowflake architectural term.

The Data Storage Layer is designed for durability, scalability, and optimal query performance.

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The PARSE_JSON() function ingests a string containing JSON text and converts it into Snowflake’sVARIANTdata type, enabling the JSON to be queried, navigated, and transformed using SQL. Snowflake does not store JSON in its raw textual representation; instead, VARIANT allows Snowflake to apply optimized parsing, indexing, and querying operations against semi-structured content. This function is particularly useful when JSON arrives inline (e.g., supplied directly within SQL statements or loaded from CSV files containing JSON strings). PARSE_JSON does not perform data loading from stages—that is handled through COPY INTO—nor does it convert JSON into XML. Once JSON is converted to VARIANT, Snowflake allows access to nested structures using dot notation, bracket notation, and functions like FLATTEN(). Thus, the function serves as a bridge between raw JSON strings and Snowflake’s relational and analytical capabilities.

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