Which command is synonymous with the LIST command in Snowflake?
LS
SHOW
DESC
LIS
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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What happens when you execute the following command in Snowflake?
DROP SCHEMA IF EXISTS marketing_schema;
An error is returned if the schema does not exist
The schema "marketing_schema" is created if it does not exist
The schema "marketing_schema" is dropped only if it exists
The schema "marketing_schema" is dropped even if it contains tables
The SQL command DROP SCHEMA IF EXISTS marketing_schema; instructs Snowflake to drop the specified schema only if it already exists in the database. The inclusion of the IF EXISTS clause prevents Snowflake from returning an error when the schema is not found. If the schemadoesexist, Snowflake drops it along with all objects it contains, unless a RESTRICT clause is enforced (RESTRICT is the default when not explicitly specified, but Snowflake also supports CASCADE for forcing deletion of all objects). Importantly, this command never creates a schema; it strictly performs a conditional drop. In addition, Snowflake supports Time Travel, so the dropped schema can typically be restored—using UNDROP SCHEMA—within the defined retention window. Therefore, the correct interpretation is that the schema is dropped only if it exists, and no error is thrown otherwise.
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Which of the following file formats can be processed by the PARSE_DOCUMENT function?
PPTX
XLSX
DOCX
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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What is the purpose of assigning roles to users in Snowflake?
To manage and enforce permissions on objects
To define user tasks
To optimize query execution plans
To control which data types users can query
Snowflake uses a Role-Based Access Control (RBAC) model, whererolesare the containers of privileges. Assigning roles to users ensures that permissions on database objects (such as tables, schemas, warehouses, and functions) are enforced consistently and securely. Users do not receive privileges directly; instead, privileges are granted to roles, and roles are assigned to users.
This enables scalable, auditable, and manageable access control.
Roles do not determine tasks, do not affect query optimization, and do not govern which data types a user may query—permissions are object-based, not datatype-based.
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Which of the following is a potential use case for the CLASSIFY_TEXT function?
Generating SQL code from natural language description
Categorizing customer support tickets based on topic
Translating a user manual into multiple languages
Extracting data from scanned invoices
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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Which of the following are examples of system-defined roles in Snowflake? (Choose any 3 options)
USERADMIN
CUSTOM_ROLE
ACCOUNTADMIN
SECURITYADMIN
Snowflake includes severalsystem-defined RBAC rolesto provide structured access control across the platform.
ACCOUNTADMINis the highest-privileged role, governing the entire account, including billing, replication, failover groups, and object ownership inheritance.
SECURITYADMINis responsible for user, role, and privilege management, including granting and revoking roles, password policies, and MFA enforcement. It is essential for identity lifecycle governance.
USERADMINmanages user creation, role creation, and user authentication administration. It is commonly assigned to identity administrators.
CUSTOM_ROLEis not a Snowflake system role; it is a user-created role, allowing organizations to design fine-grained, domain-specific access policies.
These predefined roles form the foundation of Snowflake’s security model and support least-privilege implementations.
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What does the SELECT * statement do in a Snowflake query?
Retrieves all columns from a specified table or view
Retrieves only distinct values
Retrieves only the primary key column
Retrieves a limited number of rows
The SELECT * statement instructs Snowflake to returnall columnsfrom the referenced table or view. This is commonly used during data exploration, debugging, initial data profiling, and validation steps. It allows users to quickly view the complete dataset structure without manually specifying each column name.
However, while SELECT * retrieves all columns, it does not limit the number of rows. To restrict rows, developers must include aLIMITclause (e.g., SELECT * FROM table LIMIT 10;).
The query does not automatically apply DISTINCT or primary key filtering—Snowflake returns all rows exactly as stored unless additional filtering, WHERE conditions, or ordering are provided.
Though SELECT * is convenient, Snowflake best practices recommend explicitly selecting columns in production workloads to optimize performance and avoid unnecessary scanning of unused fields.
What is the purpose of Time Travel?
To automatically manage timestamp data types
To ensure that users' data can be recovered at any time
To facilitate the loading of historical data into Snowflake
To allow users to access historical data
Time Travel enables Snowflake users to query, clone, or restore historical versions of data. This includes retrieving previous states of tables, schemas, or databases—even after updates, deletes, or drops. Time Travel operates within a retention period (default: 1 day, up to 90 days on higher editions).
Users can query historical data using the AS OF or BEFORE clause, restore dropped objects, and clone databases at specific points in time for backup or analysis.
Time Travel doesnotautomatically manage timestamp data types. It does not guarantee indefinite recovery—after the retention window expires, data moves into Fail-safe. It also is not primarily designed for loading historical datasets; its purpose is to access past states of Snowflake-managed data.
Thus, the correct purpose is to enable access to historical data inside Snowflake.
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What is a benefit of using an external stage to load data into Snowflake?
External stages reduce data storage costs because data is stored outside Snowflake.
External stages provide automatic data purging after successful loads.
External stages are more secure than internal stages for sensitive data.
External stages reduce the number of objects in a database.
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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Which SQL command is used to view the definition of a Table?
SHOW TABLE
SELECT GET_DDL('TABLE','
SELECT * FROM INFORMATION_SCHEMA.ABLES
DESCRIBE TABLE
TheGET_DDLfunction retrieves the Data Definition Language (DDL) script that was used to create a given object. Executing:
SELECT GET_DDL('TABLE', 'MY_TABLE');
returns the full DDL including column definitions, comments, clustering keys, constraints, masking policies, and other metadata. This makes it essential for schema migration, replication, compliance audits, and environment synchronization.
DESCRIBE TABLEshows only column-level metadata—names, types, nullability—but does not return the full DDL.
SHOW TABLEis not a valid Snowflake command; tables are listed using SHOW TABLES.
INFORMATION_SCHEMA.TABLES lists table metadata, but the provided option “ABLES” is invalid and still would not provide DDL even if corrected.
Thus,GET_DDL()is the only method that returns the complete table definition directly.
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What file extension is commonly used for Snowflake notebooks?
.sql
.ipnb
.ipynb
.txt
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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What is the main characteristic of Snowflake's Standard Edition?
Offers unlimited Time Travel history.
Includes Business Critical features like HIPAA compliance.
Supports Private Connectivity (AWS PrivateLink, Azure Private Link).
Provides core Snowflake features for data warehousing and analytics.
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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What information can users view in the Query History option in Snowsight?
Only successful queries
Data storage configuration
Network settings
Details of executed queries, including status, duration, and resource usage
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.
How does Snowflake process queries?
With shared-disk architecture
Using MPP compute clusters
By optimizing data in cloud storage
Through third-party connectors
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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What is the primary use of Snowflake Cortex LLM functions?
Running machine learning models on structured data.
Optimizing data loading performance.
Performing traditional SQL joins.
Analyzing text data for sentiment summarization and other AI/ML tasks.
Cortex LLM functions integrate large language model capabilities directly inside Snowflake. These functions enable:
• Sentiment analysis
• Summarization
• Document question answering
• Text classification
• Embedding generation for vector search
They operate directly on Snowflake data, require no external infrastructure, and support secure in-platform AI workloads.
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Which of the following settings can be configured for a Snowflake Virtual Warehouse? (Choose any 3 options)
Cloud provider region
Auto-suspend time
Auto-resume
Warehouse size
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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What is the primary responsibility of Role Hierarchy in Snowflake's access control model?
To allow roles to inherit privileges from parent roles
To define the order of SQL command execution
To categorize different types of users
To specify the size of virtual warehouses
Role hierarchy enables a parent role to inherit privileges assigned to child roles. This simplifies RBAC administration, reduces redundant grants, and ensures scalable security management. It has no relation to SQL sequencing, user categorization, or compute sizing.
Which data type in Snowflake is commonly used for storing semi-structured data?
BLOB
JSON
VARIANT
VARCHAR
Snowflake uses theVARIANTdata type as the native mechanism for storing semi-structured data formats such as JSON, XML, Avro, ORC, and Parquet. VARIANT supports hierarchical, nested structures and allows querying via dot or bracket path notation without requiring schema-on-write.
JSON is not a Snowflake data type—JSON documents arestored insideVARIANT.
VARIANT allows:
Mixed structured and semi-structured content
Efficient storage through Snowflake’s columnar compression
Schema-on-read flexibility
Querying nested structures without flattening
VARCHARstores plain text only.
BLOBis reserved for binary data and not suitable for interactive querying.
This design eliminates the need for preprocessing or schema enforcement at ingestion time.
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Which role is a system defined role in Snowflake?
USERADMIN
SNOWFLAKE_ADMIN
SNOWFLAKE_DBA
DATA_ENGINEER
USERADMIN is one of Snowflake’ssystem-defined roles, created automatically in every account. It is responsible for managing users and roles, including CREATE USER, ALTER USER, and role assignment. It is part of Snowflake’s default RBAC hierarchy (SYSADMIN, SECURITYADMIN, USERADMIN, etc.).
SNOWFLAKE_ADMIN and SNOWFLAKE_DBA are not Snowflake system roles—they may exist in organizations as custom roles but do not appear by default. DATA_ENGINEER is also user-created and not a built-in role.
Therefore, USERADMIN is the only true system-defined role listed.
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What are Snowflake customers responsible for?
Managing the hardware on which Snowflake is installed
Software installation and updates
Managing table metadata
Loading, unloading, and managing data
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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Which of the following is a key feature of Snowflake's Data Storage Layer? (Select 2 options)
In-memory storage
Internal optimized, compressed, columnar format
Elastic storage
Elastic Cloud
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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What is a key characteristic of a Snowflake virtual warehouse?
It provides compute resources.
It manages account roles.
It permanently stores data.
It encrypts data.
A virtual warehouse is the compute engine of Snowflake. It provides CPU, memory, and temporary storage needed to execute SQL queries, data loading operations, and DML actions. Warehouses can be sized dynamically and suspended or resumed to optimize cost.
Warehouses donotstore data; Snowflake’s storage is independent and centralized. Warehouses do not manage roles—access control is handled through Snowflake’s RBAC system. Encryption is performed automatically by Snowflake’s storage and cloud services, not by warehouses.
Thus, the correct characteristic is that virtual warehouses supply compute.
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If you'd like, I can provideQuestions 42–55 next, with the same 150–200-word explanations.
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Questions 42–55 next, with the same 150–200-word explanations
ChatGPT said:
Below areQuestions 42–55, all formatted exactly per your requirements, with150–200-word explanationsandevery original option preserved exactly.
Which SQL command is commonly used to load structured data from a stage into a Snowflake table?
INSERT INTO
COPY INTO
LOAD DATA
IMPORT DATA
The COPY INTO
