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Semi-structured data like JSON stored in a VARIANT column can be queried directly usingpath notation, either dot notation or bracket notation.

Examples:

Dot notation:SELECT data:customer.name FROM table;

Bracket notation:SELECT data['customer']['name'] FROM table;

Snowflake automatically interprets the JSON structure, making relational extraction unnecessary. Complex fields can be accessed through functions such as FLATTEN, OBJECT_KEYS, ARRAY_SIZE, and TYPEOF.

Incorrect options:

Converting JSON to a relational table is optional, not required.

Stored procedures are not necessary for JSON querying.

WHERE on the full VARIANT cannot precisely extract fields.

Thus, path notation is the native, efficient method.

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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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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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Snowflake’scompute layerusesMassively Parallel Processing (MPP), meaning queries are divided into smaller tasks distributed across multiple compute nodes in the Virtual Warehouse. Each node processes a portion of the data simultaneously, maximizing parallelism and drastically reducing query times.

Although Snowflake uses a central storage layer (shared-disk model), the compute engine behaves like ashared-nothing MPP system, where each node handles local processing independently, minimizing contention.

Incorrect options:

Snowflake does not rely on single-thread execution.

Storage optimization occurs at the Storage Layer, not compute.

Snowflake does not use traditional shared-disk execution; compute nodes work in parallel independently.

This architecture enables high performance for large analytical workloads.

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The SENTIMENT function in Snowflake Cortex analyzes text and returns a numerical sentiment score between -1 and 1. This value indicates whether the text expresses negative, positive, or neutral sentiment. It accepts English-language text and uses Snowflake’s managed AI models to infer polarity based on contextual understanding. A negative score represents negative emotion, a positive score represents positive emotion, and values near zero indicate neutrality. It is ideal for customer reviews, feedback analysis, and text mining. TRANSLATE is used for language translation, COMPLETE for text generation, and PARSE_DOCUMENT for extracting text from documents—not sentiment analysis. SENTIMENT provides fast, in-database AI inference without external model hosting or integration.

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When you preview a table in Snowsight, Snowflake returns up to the first 100 rows of the table’s data. This preview is designed for quick inspection of the table’s contents and structure without requiring the user to manually write a SELECT query. The preview interface allows basic exploration such as scrolling, simple sorting, and viewing column metadata.

Tables are not “stored” in a specific virtual warehouse; warehouses provide compute only. The table definition (DDL) can be viewed in the Table Details section but is not the main output of the Preview action. Snowsight does not natively generate an Entity Relationship (ER) diagram for the table as part of the preview.

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Snowflake’s Cloning feature provides the ability to create azero-copyclone of tables, schemas, or entire databases. This means that the cloned object references the same underlying micro-partition data as the original, so no duplicate storage costs are incurred upon creation. Instead, Snowflake uses metadata pointers to reference the existing data. If changes occur in the original or cloned objects, Snowflake performs copy-on-write operations to maintain isolation while still optimizing storage. This cloning mechanism is extremely valuable for development, testing environments, analytics validation, and troubleshooting historical states. It is not a real-time backup tool; backup and restore are handled through Time Travel and Fail-safe. Cloning does not grant or manage permissions nor copy the entire Snowflake account; it works solely at the database, schema, and table object levels.

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The Query Processing layer of Snowflake is wherevirtual warehouses operate, so warehouse sizing parameters (X-Small to 6X-Large) fall under this layer. Warehouse size determines compute power, concurrency, and performance behavior for SQL workloads. Administrators configure warehouse size based on workload intensity, response time requirements, and cost considerations.

Serverless compute limits and micro-partition limits belong to storage and services layers. Table types (permanent, transient, temporary) are storage-level configurations, not part of Query Processing.

Thus, warehouse sizing is the correct parameter configured at the Query Processing layer.

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