SOL-C01 SnowPro Associate: Platform Certification Exam Question and Answers

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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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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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.

TheSKIP_HEADERoption instructs Snowflake to ignore a defined number of initial rows, typically the column header row in CSV files. For example:

CREATE FILE FORMAT my_fmt

TYPE='CSV'

SKIP_HEADER=1;

When used with COPY INTO, Snowflake loads only the actual data rows while excluding header metadata. This prevents header values from being inserted as data and ensures consistent row structure.

Other options:

SKIP_BLANK_LINEScontrols handling of empty rows, not headers.

Snowflake does not auto-generate column names from header rows; table schemas must be predefined.

Including header rows as data is undesirable and incorrect in most pipelines.

Thus, SKIP_HEADER is used exclusively to omit header rows during ingestion.

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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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A multi-cluster warehouse raises Snowflake’s capability to handle concurrent workloads efficiently. When large numbers of users or queries hit the system, Snowflake can automatically start additional clusters (scaling out) to distribute workloads, reducing queuing and improving performance. As query demand drops, Snowflake scales in by shutting down clusters to conserve credits. This dynamic auto-scaling provides significantly increased compute capacity during peak usage periods and enhances responsiveness. Importantly, multi-cluster warehouses affect only compute, not storage, so they do not reduce storage costs. Their primary benefits revolve around performance stability, concurrency handling, workload isolation, and compute elasticity.

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Snowflake Marketplace provides multiple intuitive discovery mechanisms to help users explore available listings efficiently. Thesearch bar at the top of the Marketplaceis a primary entry point, enabling keyword-based discovery across providers, industries, and dataset types. This facilitates targeted searching for specific topics or data categories.

Universal Search, available in Snowsight, extends search beyond databases and worksheets to include Marketplace listings. This unified search experience allows users to locate datasets without navigating through menus, improving productivity and discoverability.

Browsing categories and tagssupports exploratory search by grouping listings under predefined themes such as financial data, geospatial data, demographics, and more. These tags promote structured navigation, especially useful when users are exploring rather than seeking something specific.

Contacting Snowflake Supportis not required for accessing or discovering listings and is irrelevant to Marketplace search workflows.

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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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The Public schema is created by default within every Snowflake database and serves primarily as a shared workspace that is accessible to all users who have the necessary privileges. One of its main roles is to act as adefault schemafor objects created without explicitly specifying a schema name. This ensures usability and prevents object creation errors when users forget to define a schema. Additionally, the Public schema is commonly used to storeshared or common resourcesthat multiple users or teams need to access. However, it should not be used for sensitive or user-specific data because access can be broad. It does not store account-level metadata; that information resides in INFORMATION_SCHEMA views or account-level metadata functions. Overall, the Public schema acts as both a convenient default location and a shared-access area within a Snowflake database.

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Scaling up a warehouse increases compute resources (CPU, memory), improving performance on heavy workloads: large joins, transformations, and long-running analytical queries.

It does not increase storage, reduce cost, or guarantee higher concurrency (multi-cluster handles concurrency).

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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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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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Snowflake provides intuitive mechanisms to navigate semi-structured data stored in VARIANT columns, especially JSON, XML, Parquet, and Avro. Two primary access methods aredot notationandbracket notation. Dot notation, such as column.nested.key, allows straightforward access to nested elements using a hierarchical reference. Bracket notation, such as column['key name'], allows access to elements with special characters, spaces, or numeric array indices. While Snowflake also offers powerful functions such as FLATTEN(), OBJECT_CONSTRUCT(), and ARRAY_LENGTH(), these operate on semi-structured types but do not serve as direct field-access mechanisms. Cloud Functions play no role in querying semi-structured fields inside Snowflake. Dot and bracket notation work together to provide full expressiveness, enabling users to traverse complex hierarchical structures directly in SQL, making Snowflake an ideal platform for semi-structured data analytics.

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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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InSnowsight, the Query History user interface retains and displays executed query information for up to14 days. This includes query text, status, start/end timestamps, duration, warehouse usage, and result statistics. Snowsight also offers grouping by session, user, warehouse, and time windows for detailed analytics and troubleshooting.

It is important to distinguish Snowsight UI limits from SQL-based history functions. The table functionINFORMATION_SCHEMA.QUERY_HISTORY()returns only7 daysof history, whereas Snowsight visually retains 14 days. Snowflake’s Account Usage views (e.g., QUERY_HISTORY) retain data for longer periods but are meant for programmatic auditing rather than UI display.

Incorrect options:

7 days refers only to Information Schema.

21 or 28 days are not supported by Snowsight’s UI.

Thus, the correct visibility window in the Snowsight interface is14 days.

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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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A table is the core Snowflake object used to store structured data. Tables persist rows and columns in Snowflake’s storage layer, which internally uses compressed, columnar micro-partitions managed by the platform. Tables can be permanent, transient, or temporary, but in every case, they are the primary objects that hold the actual data.

A virtual warehouse is a compute resource used to process queries and perform operations; it does not store user data. A stored procedure contains executable logic (such as complex workflows or procedural code) but does not serve as a data container. A view is a logical object that presents the results of a saved query; it references underlying tables and does not store its own data. As a result, “Table” is the correct answer.

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The DATA_RETENTION_TIME_IN_DAYS parameter controls the Time Travel retention period for Snowflake objects such as tables, schemas, and databases. It specifies the number of days that historical data (prior versions of rows or dropped objects) is retained and accessible via Time Travel. Within this retention window, users can query data “as of” a previous time, restore dropped objects, or clone objects at a historical point.

DATE_OUTPUT_FORMAT determines the display format for date values and is unrelated to historical retention. TIMEZONE affects how timestamps are interpreted and displayed, not how long data history is preserved. USE_CACHED_RESULT governs whether Snowflake may return cached query results, not Time Travel behavior.

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On the Snowflake Marketplace, users can apply a Price filter when searching for data products. This filter helps distinguish between free offerings and paid listings, making it easier to align data acquisition with budget and commercial terms. Marketplace metadata includes pricing information that this filter uses to refine search results.

Dataset “Size” is not exposed as a primary Marketplace search filter. “Compatibility” is implied, as all Marketplace products are consumable within Snowflake. “Performance” is not a filterable attribute, because performance is influenced mainly by the consumer’s compute configuration and query design, rather than a fixed property of the Marketplace listing.

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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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Snowflake is best described as anAI Data Cloud Platform. While Snowflake originated as a cloud data warehouse, its capabilities have expanded to include secure data sharing, application development, machine learning workloads, unstructured data processing, Snowpark, and integrated LLM/AI functions (such as Snowflake Cortex and Streamlit). This broad ecosystem supports analytic workloads, governance, multi-cloud architecture, and AI-driven applications. It is not simply an ETL tool or a transactional database.

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Snowflake'sINFORMATION_SCHEMAcontains metadata views about databases, schemas, tables, columns, constraints, and other objects. To retrieve a list of tables, the correct view isINFORMATION_SCHEMA.TABLES, which returns:

Table name

Schema name

Table type (BASE TABLE, VIEW, MATERIALIZED VIEW)

Creation timestamp

Owner and retention properties

The query:

SELECT * FROM INFORMATION_SCHEMA.TABLES;

returns metadata for all tables in thecurrent databasecontext.

INFORMATION_SCHEMA.COLUMNS returns column-level metadata, not table listings.

PUBLIC.INFORMATION_SCHEMA is invalid—INFORMATION_SCHEMA is not inside PUBLIC schema.

PUBLIC.TABLES is not a system object unless created manually.

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External stages point to files in external cloud storage (S3, Azure Blob, GCS). Because the data is not stored inside Snowflake, the user avoids Snowflake storage charges, which can significantly reduce cost for large staging datasets.

External stages do not automatically delete files, are not inherently more secure than internal stages, and still count as database objects even though they reference external storage.

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Snowflake warehouse sizes followT-shirt sizing, each representing a doubling of compute resources relative to the previous size. Valid Snowflake warehouse sizes includeX-Small,Small,Medium,Large, and up to6X-Large.

X-Smallis the smallest supported warehouse size and is commonly used for light data processing, development tasks, and low-volume operations.

Smalldoubles the compute capacity of X-Small, providing better performance for moderate workloads.

Mediumcontinues scaling upward, offering increased CPU, memory, and parallelism for heavier analytics or transformation workloads.

Tinyis not a Snowflake warehouse size. Snowflake never exposes a warehouse smaller than X-Small, ensuring consistent minimum compute capabilities.

These standardized size tiers allow predictable resource scaling and cost forecasting.

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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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Snowflake’s foundational security capability isalways-on end-to-end encryption, applied to all customer data both at rest and in transit. Snowflake encrypts data stored in micro-partitions using AES-256, and all network communication uses TLS encryption. Key management is automated through Snowflake’s hierarchical key model, providing additional rotation, rekeying, and defense-in-depth mechanisms.

While RBAC, masking policies, and network policies are crucial components of Snowflake security, encryption is the platform’s guaranteed, baseline, mandatory protection feature. It applies universally to all data, regardless of workload, role configuration, or user settings.

Option A: automated masking is optional and policy-driven, not a primary universal mechanism.

Option B: network perimeter defense exists but is not the platform’s core foundation.

Option C: RBAC controls access but does not protect data itself.

Thus, Snowflake’s primary and most universal security capability is end-to-end encryption.

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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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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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Standard Edition provides all essential Snowflake capabilities:

• SQL data warehousing

• Secure data sharing

• Time Travel (1 day)

• Internal/external stages

• Basic governance features

It does not include HIPAA/PCI compliance (Business Critical), nor unlimited Time Travel, nor advanced private connectivity.

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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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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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COPY INTO

SECURITYADMIN manages identity and access control, including creating, modifying, and dropping users and roles.

USERADMIN manages users but lacks full privilege management capability.

SYSADMIN manages object creation.

ACCOUNTADMIN holds full administrative privileges but delegates user/role management to SECURITYADMIN.

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Snowflake supportsNon-Materialized Views(standard views),Materialized Views, andSecure 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.

SHOW GRANTS is used to list privileges, including roles assigned to a user.

SHOW GRANTS TO USER reveals all the roles that user inherits.

SHOW ROLES lists roles, SHOW USERS lists users, SHOW DATABASES lists databases—not SHOW GRANTS.

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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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Zero-Copy Cloning allows Snowflake users to create instant, writable clones of databases, schemas, or tableswithout physically copying data. Instead, Snowflake references the same underlying micro-partitions, making the clone creation nearly instantaneous and storage-efficient. New storage is consumed only when updates apply to either the original or the clone, using copy-on-write mechanics. It does not perform physical copies, does not sync data in real-time, and does not freeze the clone from modification.

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PARSE_DOCUMENT extracts text and, depending on mode, layout information from supported document formats. The function currently supportsPDF,PPTX, andDOCXfiles. For PDFs, it can perform OCR to extract scanned text or use LAYOUT mode to detect tables and structured regions. For PPTX files, it extracts slide text and content. For DOCX files, the function captures text and applies layout analysis. XLSX files are not supported—Excel content requires different extraction methods. PARSE_DOCUMENT enables automated document intelligence workflows entirely inside Snowflake, eliminating the need for external OCR or parsing services.

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A Reader Account allows data providers to share data with recipients who do not have their own Snowflake subscriptions. Providers manage and pay for compute and storage used by the reader account. Reader accounts are limited to querying shared data and cannot alter Snowflake objects. They are not for replication, internal operations, or external stage reading.

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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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COPY INTO

Snowsight is the default web-based interface for Snowflake. It provides a graphical, browser-based environment to write and run SQL and Python, manage and explore database objects, create visualizations and dashboards, monitor query and warehouse activity, and collaborate via worksheets. It has replaced the Classic Console as the default UI for new Snowflake accounts.

SnowSQL is a command-line client used to connect to Snowflake from a terminal and execute SQL statements. Snowpark is a developer framework that supports writing data applications in languages like Python, Java, and Scala, but it is not a UI. “Snowflake CLI” refers to command-line tooling and automation utilities, again not a graphical web interface.

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The Data Exchange feature is not automatically enabled for all Snowflake accounts. Instead, enabling this capability—particularly for private exchanges—typically requires submitting a request toSnowflake Support. Snowflake evaluates account type, region, and compliance requirements before enabling the Data Exchange functionality. Snowflake administrators cannot independently enable this through Snowsight, nor is there a SQL command available to turn it on. Once activated, account administrators can create private exchanges, manage listings, and define which Snowflake accounts can participate. This activation step ensures secure, intentional use of data-sharing capabilities at an account level.

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The only valid Snowflake SQL command among the options forcreating a schemais:

CREATE SCHEMA schema_name;

This command creates a schema within the currently active database. Optional modifiers like IF NOT EXISTS or OR REPLACE can be used to control behavior.

Other options:

USE SCHEMAswitches to an existing schema; it does not create one.

USE SCHis invalid because SCH is not a Snowflake keyword.

CREATE SCHis invalid; SCH is not a valid abbreviation.

Thus, the only valid schema creation command isCREATE SCHEMA.

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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’s architecture consists of:

• Database Storage Layer – Stores optimized columnar data.

• Query Processing Layer – Executes workloads via virtual warehouses.

• Cloud Services Layer – Handles metadata, optimization, access control.

ETL tools and UI are external integrations. Snowflake is cloud-native and has no on-premises components.

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COPY INTO @stage writes query results or table data into files stored in either internal or external stages.

Unloading cannot target a local filesystem directly, cannot load into another table (use INSERT instead), and does not write to streams.

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A virtual warehouse in Snowflake is acompute resourcethat supplies CPU, memory, and temporary storage used to process SQL queries, perform data loading, execute DML operations, and run procedural logic. Warehouses are elastic, can be resized on demand, suspended to save costs, and automatically resumed when needed.

Virtual warehouses donotstore data—data is kept in Snowflake’s centralized storage layer. They also are not user accounts; user accounts belong to Snowflake’s identity management system. Worksheets are UI components in Snowsight or Classic Console used to write and run SQL, not compute resources.

Thus, virtual warehouses are Snowflake's compute engine, enabling all processing without managing physical infrastructure.

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The COPY INTO

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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By default, Snowflake usesON_ERROR = ABORT_STATEMENTfor COPY INTO. This means that if a single row fails to load due to issues such as type mismatches or malformed data, Snowflake immediately stops the entire load operation. This behavior ensures data integrity by preventing partial or incomplete loads when errors occur.

Other error-handling modes must be explicitly specified: CONTINUE skips erroneous rows, SKIP_FILE skips whole files with errors, and VALIDATE is part of the validation mode feature, not a default COPY behavior.

Thus, unless explicitly changed, COPY INTO always uses ABORT_STATEMENT.

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Scaling in/outrefers tohorizontal scaling, which is available only inmulti-cluster warehouses. When Snowflake scalesout, additional compute clusters are added automatically to handle high concurrency, preventing query queuing. When load decreases, Snowflake scalesin, reducing cluster count to save compute credits.

This differs fromscaling up/down, which refers to changing warehouse size (X-Small → Small → Medium). Vertical scaling increases compute resources for a single cluster but does not improve concurrency for simultaneous workloads.

Scaling in/out is valuable for BI dashboards, workloads with many simultaneous users, peak-hour query loads, and applications requiring consistent performance under high concurrency.

Changing regions is an account-level configuration and unrelated to warehouse scaling.

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TheCOPY INTOcommand supports loading structured and semi-structured data from various Snowflake stages. Supported file formats includeCSV,JSON,Parquet,Avro,ORC, and others. Each format integrates with Snowflake’s file format definitions, enabling configuration for delimiters, compression, headers, and parsing rules.

CSVis widely used for tabular datasets and supports flexible parsing via FILE_FORMAT options such as FIELD_DELIMITER, SKIP_HEADER, and NULL_IF.

JSONis loaded natively into VARIANT columns, allowing nested structures to be queried immediately through path notation.

Parquet, a high-efficiency columnar format, is optimized for analytic workloads and provides excellent compression and schema evolution benefits.

XLS is not supported, as Snowflake does not support Excel spreadsheets directly. XLS/XLSX files must be converted to CSV or Parquet before loading.

The COPY INTO architecture ensures performant ingestion across multi-node compute clusters.

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To create a new schema in Snowsight, users must follow the hierarchical navigation of Snowflake’s object structure. The correct sequence is:

Data → Database → Create Schema

Steps:

Open theDatatab in the left navigation.

Select aDatabasewhere the schema will be created.

ChooseCreate → Schema.

Enter the schema name and (optionally) description.

ClickCreate.

Incorrect options:

“Add Data” is used for loading files, not creating schema objects.

“Data → Create Schema” is incomplete because Snowflake must knowwhich databasethe schema belongs to.

“Data Product” is unrelated to object creation.

Thus, Snowflake requires explicit database context before schema creation.

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