SnowPro exam questions are packed with specialized terminology: the three-layer architecture, Virtual Warehouse, RBAC, Time Travel, Data Sharing, and more. Accurate understanding of terminology is the foundation of passing. This article curates 80 essential exam terms and organizes them into 5 categories — Architecture, SQL, Security, Data Sharing, and Performance — explained in scannable table format.
Terms covering Snowflake's three-layer architecture and platform composition. This is the highest-frequency topic on the SnowPro Core exam, weighted at 25-30% in Domain 1.
| No. | Term | Description |
|---|---|---|
| 1 | Snowflake AI Data Cloud | Unifies data warehousing, data lakes, data sharing, and ML/AI workloads on a single platform. Runs on AWS, Azure, and GCP. |
| 2 | Three-Layer Architecture | Storage Layer, Compute Layer, and Cloud Services Layer are fully decoupled and scale independently. Snowflake's defining characteristic. |
| 3 | Storage Layer | Handles persistent storage. Data is compressed, encrypted, and stored on the cloud provider's object storage (S3 / Azure Blob / GCS) as micro-partitions. |
| 4 | Compute Layer | Handles query execution. Provides independent compute clusters as Virtual Warehouses, fully decoupled from storage. |
| 5 | Cloud Services Layer | Handles metadata management, authentication, authorization, query optimization, and transaction management. Result Cache and Metadata Cache also live here. |
| 6 | Micro-partition | The unit of data storage. Columnar 50-500 MB compressed data blocks. Automatically managed by Snowflake — no user definition required. Their metadata drives pruning. |
| 7 | Virtual Warehouse | The unit of compute resources, sized from X-Small to 6X-Large. Each size step roughly doubles resources. Supports auto-suspend/resume. |
| 8 | Multi-cluster Warehouse | Enterprise edition and above. Two modes: Auto-scaling (clusters scale with load) and Maximized (always at maximum clusters). Handles concurrent query workloads. |
| 9 | Snowflake Editions | Four tiers: Standard, Enterprise, Business Critical, and VPS. Higher tiers add security features and have higher credit unit pricing. |
| 10 | Credit | The unit of compute consumption. Calculated as Warehouse runtime × size. Also consumed by Serverless features and excessive Cloud Services usage. |
| 11 | Snowpark | Framework for executing data processing in Python, Java, and Scala. Used to build DataFrame API code, UDFs, and Stored Procedures. |
| 12 | Snowflake Marketplace | A marketplace for discovering and acquiring third-party datasets and services. Access external data directly with no data copy. |
| 13 | Snowflake Cortex | Umbrella name for Snowflake's AI/ML capabilities. Includes LLM functions (COMPLETE / TRANSLATE / SUMMARIZE), ML Functions (FORECAST / ANOMALY_DETECTION), and Search features. |
| 14 | Native App Framework | Framework for building and distributing applications on Snowflake. Composed of an Application Package (manifest.yml, setup.sql, Stage). |
| 15 | Snowgrid | Snowflake's global data network. The foundation for cross-region and cross-cloud replication, failover, and Data Sharing. |
| 16 | Snowsight | The web-based UI. Provides SQL query execution, visualization, dashboards, and account management. The successor to the legacy Classic Console. |
Snowflake-specific SQL extensions and data manipulation terms. Frequently tested on the Data Transformations and Data Analyst exams.
| No. | Term | Description |
|---|---|---|
| 17 | QUALIFY clause | A Snowflake-specific SQL extension that filters on window function results. Lets you filter ROW_NUMBER() output directly without a subquery. |
| 18 | FLATTEN function | A table function that expands VARIANT, ARRAY, and OBJECT values into rows. Used in the form TABLE(FLATTEN(input => data)), often combined with a LATERAL clause. |
| 19 | VARIANT type | A general-purpose type for semi-structured data (JSON, XML, Avro, Parquet, etc.). Up to 16 MB. Accessed via colon notation (data:key:subkey). |
| 20 | COPY INTO | SQL for bulk loading and unloading data between stages and tables. Flexibly configured via FILE FORMAT, ON_ERROR, and VALIDATION_MODE options. |
| 21 | Stored Procedure | Written in JavaScript, Python, Java, or Scala. Implements DDL/DML execution, branching, loops, and exception handling. Invoked via the CALL statement. |
| 22 | UDF / UDTF | User-defined functions. Scalar (UDF) and table (UDTF) variants. Can be written in SQL, JavaScript, Python, or Java. |
| 23 | Streams | A CDC object that captures table changes (INSERT/UPDATE/DELETE). Three types: Standard, Append-only, and Insert-only. |
| 24 | Tasks | An object that runs SQL statements or stored procedures on a schedule, defined by CRON or minute interval. Dependencies are defined via task trees. |
| 25 | Snowpipe | A serverless service that auto-loads data when files land in a stage. Triggered by event notifications (SQS / Event Grid / Pub/Sub). |
| 26 | Dynamic Table | Declaratively defined with target_lag controlling refresh frequency. Auto-incrementally refreshes as source data changes. An alternative to Streams + Tasks. |
| 27 | External Table | A read-only table that queries files on an external stage directly. Lets you analyze data on S3 / Azure / GCS without loading it first. |
| 28 | Materialized View | A view whose query results are physically stored and automatically incrementally refreshed. Enterprise edition and above. Used to accelerate complex aggregations. |
| 29 | MERGE INTO | SQL for upsert (UPDATE + INSERT) operations. Matches source and target on join conditions, branching via WHEN MATCHED / NOT MATCHED clauses. |
| 30 | PIVOT / UNPIVOT | PIVOT turns rows into columns; UNPIVOT turns columns into rows. Used for cross-tabulation and wide/long format conversions. |
| 31 | Window function | Aggregates, ranks, and computes moving values per partition via the OVER clause. ROW_NUMBER, RANK, LAG, LEAD, etc. Often combined with QUALIFY. |
| 32 | Sequence | An object that generates unique numbers. Used to generate surrogate keys. Get the next value with my_seq.NEXTVAL. |
Terms covering access control, authentication, and encryption. Tested in Domain 2 (Account Access & Security / 20-25%).
| No. | Term | Description |
|---|---|---|
| 33 | RBAC | Role-Based Access Control. Privileges are granted to roles, and roles are assigned to users. Snowflake's primary access control model. |
| 34 | DAC | Discretionary Access Control. Object owners grant privileges to other roles. The creating role automatically becomes the OWNER. |
| 35 | ACCOUNTADMIN | The top-level system-defined role. Combines SYSADMIN and SECURITYADMIN privileges. Not recommended for daily use. MFA strongly recommended. |
| 36 | SYSADMIN | Role for creating and managing databases and Warehouses. Recommended for day-to-day object management. |
| 37 | SECURITYADMIN | Role responsible for security management of roles, privileges, and users. Owns GRANT / REVOKE administration. |
| 38 | USERADMIN | Role specialized for creating and managing users and roles. |
| 39 | Network Policy | IP-address-based access control. Restricts source IPs via ALLOWED_IP_LIST and BLOCKED_IP_LIST. Applied at the account or user level. |
| 40 | MFA | Multi-Factor Authentication using Duo Mobile. Strongly recommended for ACCOUNTADMIN. Can also be enforced at the account level. |
| 41 | SSO | Single Sign-On based on SAML 2.0. Integrates with Okta, Azure AD, and ADFS. Federated Authentication. |
| 42 | Row Access Policy | Row-level access control. Automatically filters visible rows based on the querying user's role. Enterprise edition and above. |
| 43 | Dynamic Data Masking | Column-level data masking. Controls visibility of sensitive data based on the querying role. Enterprise edition and above. |
| 44 | Tri-Secret Secure | Business Critical edition and above. Composite encryption using a Snowflake-managed key plus a customer-managed key (KMS). Customers can block access by disabling their key. |
| 45 | Object Tagging | Tag objects to classify data. Tag-based Masking Policies enable automatic, tag-driven masking. |
| 46 | Snowflake Scripting | A SQL-based procedural extension. Supports IF/ELSE, LOOP, FOR, WHILE, and TRY-CATCH inside Stored Procedures. Also supports Anonymous Blocks. |
| 47 | AWS PrivateLink | Business Critical edition and above. Private connectivity that keeps Snowflake traffic inside the VPC instead of crossing the public internet. |
| 48 | Aggregation Policy | A policy that enforces a minimum aggregation level on shared data. Blocks viewing individual rows and permits only group-level aggregates. |
Test your terminology with practice questions
Check your level with questions covering every Snowflake exam
Try free questions →Terms covering data sharing, collaboration, and data protection. Tested in Domain 6 (Data Protection & Data Sharing).
| No. | Term | Description |
|---|---|---|
| 49 | Secure Data Sharing | Share data with other Snowflake accounts without copying it. The provider creates a Share, then the consumer creates a database from it. Real-time sharing. |
| 50 | Share | The unit object for data sharing. Add tables, views, and UDFs to a Share, then GRANT to consumer accounts. |
| 51 | Reader Account | A read-only account for organizations without their own Snowflake account. The provider creates and manages it and pays the compute costs. |
| 52 | Listing | The unit of a data product on the Marketplace. Three types: Free, Personalized, and Paid. How providers distribute data broadly. |
| 53 | Data Clean Room | An environment where multiple organizations jointly analyze data without directly viewing each other's data. Enables cross-party analysis while preserving privacy. |
| 54 | Data Exchange | An invite-only private data sharing group. Suited for sharing only among specific members. |
| 55 | Time Travel | Reference or restore data from any point in the past. Specified with AT / BEFORE clauses. Up to 1 day on Standard, up to 90 days on Enterprise+. |
| 56 | Fail-safe | A safety net for data recovery after Time Travel expires. Fixed at 7 days. Recoverable only by Snowflake support — users cannot access it directly. |
| 57 | Zero-copy Clone | Instantly creates a logical copy of a table, schema, or database. No physical copy occurs. Additional cost accrues only for changed data. |
| 58 | Replication | Replicates databases, schemas, and account objects across regions and clouds. Used for DR, distributed reads, and regulatory compliance. |
| 59 | Failover | Switches to the secondary when the primary fails. Business Critical edition and above. Combined with replication to implement BCP. |
| 60 | DATA_RETENTION_TIME_IN_DAYS | Parameter that configures the Time Travel retention period. Default 1 day, up to 90 days on Enterprise+. Settable at account / database / schema / table level. |
| 61 | Transient table | A table with no Fail-safe period and up to 1 day of Time Travel. Suited for staging and intermediate data. Reduces storage costs. |
| 62 | Temporary table | A session-scoped table. Automatically dropped when the session ends. Invisible to other users. No Fail-safe. |
| 63 | UNDROP | Command to restore a table, schema, or database dropped within the Time Travel period. Requires renaming if an object with the same name already exists. |
| 64 | Secure View | A view whose definition (SQL) is hidden. Prevents information leakage through query optimization when sharing data. Definition is not shown by SHOW VIEWS. |
Terms covering performance optimization and cost management. Tested in Domain 3 (Performance Concepts / 10-15%).
| No. | Term | Description |
|---|---|---|
| 65 | Result Cache | Query result cache managed by the Cloud Services Layer. Returns results without a Warehouse if the same query runs again within 24 hours. Invalidated when the underlying data changes. |
| 66 | Metadata Cache | Managed by the Cloud Services Layer. Returns aggregates like COUNT(*), MIN, and MAX instantly from metadata. No Warehouse required. |
| 67 | Warehouse Cache | Local SSD on the Compute Layer. Retains micro-partitions accessed by recent queries. Cleared when the Warehouse is suspended. |
| 68 | Clustering Key | Physical sort key for table data. Set via ALTER TABLE ... CLUSTER BY. Setting it on filter columns improves pruning efficiency. Aimed at large tables. |
| 69 | Partition Pruning | Optimization that skips micro-partitions not matching the query predicates. Decided using min/max metadata. Applied automatically. |
| 70 | Query Profile | Visualizes the query's execution plan and statistics. Shows processing time, scan volume, spilling, and pruning effectiveness. Essential for identifying bottlenecks. |
| 71 | Spilling | When memory is insufficient, data spills to local disk or remote storage. Resolved by sizing up the Warehouse. |
| 72 | Resource Monitor | Monitors and limits credit consumption. Sets monthly/daily caps and automatically notifies, suspends, or terminates when thresholds are reached. |
| 73 | Auto-suspend / Auto-resume | Automatically suspends when idle and resumes on a new query. Default is 10 minutes (600 seconds). A baseline cost-optimization setting. |
| 74 | Search Optimization Service | A service that accelerates point-lookup queries (equality, LIKE, IN). Automatically builds search indexes. Enterprise edition and above. Incurs additional cost. |
| 75 | Stage | A temporary holding location for data files. Two types: internal (Snowflake-managed) and external (S3 / Azure / GCS). The source/destination for COPY INTO. |
| 76 | File Format | Configures file formatting for COPY INTO. Supports CSV, JSON, Parquet, Avro, ORC, and XML. Defines delimiters, headers, and compression. |
| 77 | Storage Integration | Centralizes credentials for external cloud storage. Consolidates IAM role and service principal configuration. |
| 78 | Snowpark Container Services | Runs Docker containers inside Snowflake. Used for GPU-accelerated deep learning and custom application hosting. |
| 79 | Snowflake Notebooks | Jupyter-like notebooks inside Snowflake. Mix Python and SQL. Interactively run Snowpark ML and Cortex. |
| 80 | Iceberg Table | Open tables in the Apache Iceberg format. The same data is also accessible from engines outside Snowflake (Spark, etc.). |
Snowflake terms are frequently tested as contrasting pairs. Learning the following pairs together makes them stick and prepares you for comparison questions.
| Contrasting pair | Key difference |
|---|---|
| Result Cache vs Warehouse Cache | Cloud Services Layer vs Compute Layer. Whether it survives a suspend or not. |
| Time Travel vs Fail-safe | User-accessible vs accessible only via Snowflake support. |
| Permanent vs Transient tables | Fail-safe vs no Fail-safe, with corresponding storage cost differences. |
| RBAC vs DAC | Role-mediated privilege management vs direct grants by the owner. |
| Scale-up vs Scale-out | Faster individual queries vs higher concurrency. |
| Snowpipe vs COPY INTO | Continuous automatic loads vs batch bulk loads. |
| Dynamic Table vs Streams+Tasks | Declarative pipeline vs imperative pipeline. |
"Which edition is required for this feature?" is a frequent question pattern. The mappings below are must-memorize.
| Feature | Required edition |
|---|---|
| Time Travel up to 90 days | Enterprise and above |
| Multi-cluster Warehouse | Enterprise and above |
| Row Access Policy / Dynamic Data Masking | Enterprise and above |
| Materialized View | Enterprise and above |
| Search Optimization Service | Enterprise and above |
| Tri-Secret Secure | Business Critical and above |
| AWS PrivateLink / Azure Private Link | Business Critical and above |
| Failover / Failback | Business Critical and above |
Exams sometimes present acronyms in their fully spelled-out form. Memorize both the acronym and its expansion for terms like RBAC (Role-Based Access Control), DAC (Discretionary Access Control), MFA (Multi-Factor Authentication), SSO (Single Sign-On), and VPS (Virtual Private Snowflake).
SnowPro Core - Architecture
問題 1
Which statement about Snowflake's Warehouse Cache (Local Disk Cache) is correct?
正解: B
The Warehouse Cache (Local Disk Cache) is held on the local SSD of the Compute Layer (Virtual Warehouse). It retains micro-partition data accessed by recent queries and speeds up access to that same data (B: correct). Option A describes the Result Cache. Option C describes the Metadata Cache. Option D contradicts how the Warehouse Cache actually behaves — suspending the Warehouse clears the cache. The differences between the three caches (Result / Metadata / Warehouse) in terms of management layer and retention conditions are frequently tested on the Core exam.
In what order should I learn these terms?
Priority depends on which exam you're targeting. For SnowPro Core, prioritize terminology around the three-layer architecture (Storage / Compute / Cloud Services Layer), Virtual Warehouse, RBAC, and Time Travel. Domain 1 (Architecture / 25-30%) and Domain 2 (Security / 20-25%) together account for roughly 50% of the exam, so starting with these two domains is the most efficient approach. For the Advanced: Data Engineer exam, focus first on data pipelines, Streams/Tasks, Snowpipe, and Dynamic Tables.
Should I memorize technical terms in my native language or in English?
All SnowPro exams are delivered in English only, so we recommend memorizing terms in English. Proper nouns like Virtual Warehouse, Time Travel, Fail-safe, Micro-partition, Clustering Key, and Result Cache should be learned exactly as they appear. Use any translations only as a conceptual aid, and aim to instantly grasp the meaning the moment you see the English term. Mastering roughly 200-300 technical terms will let you understand most exam questions.
Can I pass the exam just by memorizing the glossary?
Memorization alone is rarely enough to pass. SnowPro exams center on conceptual understanding and applied reasoning — straight definition questions ("What is X?") are relatively rare. Most questions are comparative or judgment-based: "Which option is best in this situation?" or "What is the difference between these two features?" You need to understand not just definitions but the relationships between terms and when to use each one. Treat the glossary as a foundation, then deepen your understanding by reading the official documentation and doing hands-on practice.
Check your understanding with practice questions
Check your level with questions covering every Snowflake exam
Try free questions →Related Snowflake Certification Articles
Snowflake Certifications Overview
All 11 exams: scope, fees, and prerequisites
SnowPro Core: Complete Guide
Strategy guide for Snowflake's entry-level certification
Snowflake Exam Sample Questions
Sample questions and detailed explanations at every level
How to Study for Snowflake Certifications
Fastest path to passing and recommended study time
Practice with certification-focused question sets
無料で問題を解いてみるNicheeLab Editorial Team
NicheeLab editorial team focused on data engineering and cloud certification learning. Content is structured around practical study needs and official exam domains.
Snowflake Certifications: All 11 Exams Explained (2026)
Every SnowPro certification — Associate, Core, Specialty, Ad...
Snowflake Exam Difficulty Ranking: All 11 Certs Compared (2026)
All 11 SnowPro exams ranked by difficulty with study-time es...
Snowflake Study Guide: Fastest Pass Route by Exam (2026)
How to pass SnowPro certifications efficiently — official ma...
SnowPro Core (COF-C03): Complete Exam Guide (2026)
Pass the SnowPro Core exam — six domains, scope, sample ques...
SnowPro Associate Platform (SOL-C01): Complete Guide (2026)
The entry-level SnowPro Associate exam — scope, weighting, s...