Free SnowPro Associate: Platform Certification Exam Practice Questions

Snowflake’s multi-cluster shared data architecture enablesindependent scaling of compute and storage, meaning workloads can be sized differently without affecting stored data.

It also providesnear-unlimited concurrency, because Snowflake can automatically add compute clusters during peak loads.

Snowflake does not require manual infrastructure management and runs fully in the cloud—not on-premises. Multi-cloud support reduces vendor lock-in, not increases it.

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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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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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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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ThePARSE_DOCUMENTfunction offers two primary processing modes:OCRandLAYOUT, each designed for different document-intelligence tasks.

OCR (Optical Character Recognition)mode extracts raw text from scanned or image-based documents. This is ideal for contracts, scanned PDFs, receipts, manuals, and any scenario where the primary goal is text extraction. OCR focuses on textual content without preserving document structure.

LAYOUTmode extracts both text and structural context, including tables, section groupings, paragraph blocks, and visual positioning. This provides layout-aware data, making it vital for downstream use cases such as Retrieval-Augmented Generation (RAG) pipelines, structured extraction, and content summarization while retaining relationships between document components.

CONTENTis not a mode—it is a JSON key in output.OMR(Optical Mark Recognition) is not supported within PARSE_DOCUMENT.

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