SOL-C01 Exam Dumps - SnowPro Associate: Platform Certification Exam

Unstructured data refers to content that lacks a predefined schema or consistent structural pattern. It cannot be represented naturally in tabular or hierarchical formats without extraction or transformation. Snowflake supports unstructured data through external tables and native features allowing storage and machine learning analyses.Images,videos, andPDFsfall into this category because they contain raw multimedia or document content without intrinsic row/column structure, often requiring OCR, video/audio processing, or content extraction. In contrast, JSON is semi-structured, containing keys, hierarchical structures, and metadata that Snowflake’s VARIANT type can interpret directly. Unstructured data is becoming increasingly important for AI-driven workloads, enabling use cases such as OCR with PARSE_DOCUMENT, deep-learning analysis of images and video, and document intelligence tasks.

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The central difference between aStandard Warehouseand aMulti-cluster Warehouselies inhorizontal scalability and concurrency handling. A Standard Warehouse consists of one compute cluster. Though it can scaleup or down vertically(e.g., Small → Medium), it cannot automatically add more clusters during periods of heavy workload. This means that under peak concurrency, queries can queue until compute resources are available.

AMulti-cluster Warehouse, by contrast, supportsautomatic horizontal scaling, allowing Snowflake to add or remove clusters dynamically based on query demand. This ensures consistent performance under simultaneous workloads, making it essential for high-concurrency analytical environments or BI dashboards.

Both warehouse types share characteristics such as auto-resume, auto-suspend, and compatibility with all query workloads. They differ only in concurrency scaling. Multi-cluster warehouses can still auto-suspend at the warehouse level; the belief they cannot is incorrect. Standard Warehouses are fully functional for all workloads—they are not restricted to loading tasks.

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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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The CREATE FILE FORMAT command creates a file format object in Snowflake that defines how data files should be interpreted when loading or unloading. Parameters in the definition include file type (for example, CSV, JSON, PARQUET), field delimiters, compression, quoting rules, and other parsing options. Once created, this file format object can be referenced by COPY INTO commands or external table definitions to ensure consistent handling of files.

Deleting a file format is performed using DROP FILE FORMAT. Modifying an existing file format is done with ALTER FILE FORMAT. Creating a new table is achieved with CREATE TABLE. Therefore, the purpose of CREATE FILE FORMAT is specifically to define how Snowflake should process data files.

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Snowflake Marketplace provides multiple intuitive discovery mechanisms to help users explore available listings efficiently. Thesearch bar at the top of the Marketplaceis a primary entry point, enabling keyword-based discovery across providers, industries, and dataset types. This facilitates targeted searching for specific topics or data categories.

Universal Search, available in Snowsight, extends search beyond databases and worksheets to include Marketplace listings. This unified search experience allows users to locate datasets without navigating through menus, improving productivity and discoverability.

Browsing categories and tagssupports exploratory search by grouping listings under predefined themes such as financial data, geospatial data, demographics, and more. These tags promote structured navigation, especially useful when users are exploring rather than seeking something specific.

Contacting Snowflake Supportis not required for accessing or discovering listings and is irrelevant to Marketplace search workflows.

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The USE SCHEMA command sets the active schema context for the current session. After it is executed, any unqualified object names (for example, SELECT * FROM my_table) are resolved within that schema. This reduces the need to fully qualify object names with database and schema each time and ensures that statements reference the expected logical container.

CREATE SCHEMA is used to create a new schema. ALTER SCHEMA and GRANT OWNERSHIP are used to modify schema properties or transfer ownership, respectively. USE SCHEMA does not alter structure or ownership; it simply changes the context in which subsequent SQL statements are interpreted.

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The Pruning section of the Snowsight Query Profile showshow Snowflake eliminated unnecessary micro-partitions from the scan phaseof the query. Snowflake stores data in micro-partitions and maintains metadata such as min/max values for each column within each partition. When a query includes filters (e.g., WHERE clauses), Snowflake evaluates this metadata to determine which micro-partitions cannot possibly satisfy the predicate. These partitions are skipped, meaning they are never scanned or read from storage.

This process drastically improves performance because Snowflake minimizes I/O, reduces compute usage, and shortens execution time. Partition pruning is especially impactful on large tables because only a fraction of the stored micro-partitions typically need to be accessed.

The Pruning section does not show removedrows—that happens during the filter step. It does not show removedcolumns—column pruning is handled separately by the optimizer. It also does not show removedobjectsfrom the plan. Its sole purpose is to document micro-partition elimination and scan reduction.

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Internal stages are fullySnowflake-managed storage locationsused to temporarily store data files for loading or unloading operations. Examples include:

User stages (@~)

Table stages (@%table_name)

Named internal stages (@my_internal_stage)

Because they resideinside Snowflake, users do not need AWS, Azure, or GCP credentials to access them. Internal stages support all file types Snowflake can process, including CSV, JSON, Parquet, Avro, ORC, images, PDFs, and other semi-structured formats.

Incorrect statements:

They arenot limitedto structured or semi-structured data—they accept all supported formats.

External cloud credentials are only needed for external stages (S3, Blob, GCS).

Internal stages simplify secure data ingestion and operational pipelines because Snowflake handles encryption, metadata, and lifecycle management.

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