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SnowflakeCortex functionsenable AI-powered operations directly within SQL and Python, supporting multiple languages via translation, classification, summarization, and document extraction functions. They operate seamlessly onstructuredandsemi-structureddata stored in VARIANT columns, allowing LLMs to process text, JSON, documents, and rich content.

Cortex functions can be combined within SQL pipelines—for example, using PARSE_DOCUMENT to extract data, followed by CLASSIFY_TEXT to categorize extracted fields, and then SUMMARIZE to produce narrative summaries. This composability enables sophisticated multi-stage workflows for document intelligence, support ticket analysis, compliance automation, and semantic enrichment.

They arenot limited to a single domain; Cortex functions apply to finance, retail, support operations, healthcare, legal analysis, and general AI-assisted data processing.

Thus, Cortex functions are versatile, multi-domain, multi-language tools integrated with Snowflake’s compute and governance layers.

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TheLIMITclause is used to restrict the number of rows returned by a query. The correct syntax in Snowflake is:

SELECT * FROM employees LIMIT 10;

This retrieves the first 10 rows of the result set. LIMIT appears at the end of the SELECT statement. Snowflake follows a SQL syntax similar to MySQL/PostgreSQL regarding LIMIT usage.

Incorrect forms include:

Using LIMIT before SELECT

Embedding LIMIT inside WHERE

Reversing the order of SELECT and LIMIT

LIMIT is frequently used for sampling, record previewing, debugging, and exploratory analysis.

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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A Snowflake database is a top-level container that can include schemas, and within those schemas it can contain objects such astables,views,stages,functions,procedures, anddatabase roles. However,global account-level roles(such as SYSADMIN, SECURITYADMIN, PUBLIC, or custom account roles) do not reside inside a database. These roles are created and managed at the Snowflake account level, not within any specific database. In contrast,database rolesare scoped within a specific database and can exist inside it. Views and tables are standard database objects. Therefore, the only option that a Snowflake database cannot contain is an account-levelRole.

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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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Snowflake cloning is a metadata-only operation at creation. Both the clone and source share micro-partitions until changes occur (copy-on-write).

They are independent and do not auto-sync.

They can be made at the database, schema, or table level.

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Unstructured data such as PDF files, images, and other binary documents is stored in stages in Snowflake. These stages may be internal stages, which Snowflake manages directly, or external stages, which reference external cloud storage such as Amazon S3, Azure Blob Storage, or Google Cloud Storage. Stages are the designed mechanism for storing and accessing unstructured files so that they can be processed with functions like PARSE_DOCUMENT or accessed via directory tables.

External tables are used to query structured or semi-structured data (for example, Parquet or JSON) stored in external locations, not to store raw unstructured binary content. The Cloud Services layer coordinates metadata, security, and query services; it does not store user data. Tables with a single VARCHAR column might be used as an improvised approach for small text blobs, but this is not the native or recommended method for managing unstructured data at scale.

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To monitor virtual warehouse performance in Snowsight, the correct approach is using theQUERY_HISTORYview or its Snowsight equivalent under the Activity section. QUERY_HISTORY provides detailed metrics on execution time, queueing, credit consumption, compilation time, and warehouse utilization for every executed query. By analyzing this information, users can diagnose issues such as concurrency bottlenecks, under-sized warehouses, long-running queries, or excessive I/O.

SHOW WAREHOUSES returns metadata about warehouse configurations (size, state, scaling policy) but does not provide performance data. LOAD_HISTORY tracks COPY INTO operations only and is not representative of general warehouse workload. DESCRIBE WAREHOUSE shows configuration details but no runtime or historical performance metrics.

Thus, the most accurate and complete way to assess warehouse performance—particularly execution delays, queuing, or excessive credit usage—is by using QUERY_HISTORY.

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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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On the Snowsight Schema Details page, one of the supported operations is renaming the schema. The UI exposes controls that allow users with appropriate privileges to change the schema name, which can help maintain consistent naming conventions or reflect project reorganizations.

Sharing data with other accounts is typically done using secure shares at the database or object level, not from a simple “Share this schema” function on the Schema Details page. Truncation is a table-level operation (e.g., TRUNCATE TABLE), not something that applies at the schema level. Metrics visible on the Schema Details page (such as object counts or storage usage) are informational and not directly editable; they are derived from system metadata.

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