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

Snowflake is a fully managed, cloud-native platform that automates nearly all infrastructure tasks. Two defining characteristics are:

1. Snowflake handles platform maintenance, management, and upgrades.

Snowflake automatically performs software updates, scaling operations, tuning, optimization, and patching. Users never need to upgrade hardware, install software, or manage clusters.

2. There is no infrastructure for customers to provision and maintain.

All compute, storage, and services run on public clouds (AWS, Azure, GCP). Snowflake abstracts the underlying infrastructure completely, eliminating the need for customers to manage VMs, disks, clusters, or networking.

Incorrect options:

• Snowflake supportsbothcolumn-level security (via masking policies)androw-level security (via row access policies).

• Snowflake is not responsible foralldata security—customers must configure RBAC, masking, and governance.

• Snowflake doesnotrun on-premises; it is exclusively cloud-based.

Thus, options C and D correctly describe Snowflake platform characteristics.

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The COMPLETE function enables natural language generation within Snowflake Cortex. It accepts a prompt and produces a text-based completion using an underlying large language model (LLM). COMPLETE can generate single-turn or multi-turn responses, depending on how the prompt is structured. It is used for tasks such as drafting summaries, generating recommendations, writing structured output such as JSON, or extending existing text. It is not a classification tool—that role is served by CLASSIFY_TEXT—and it does not perform linguistic translation (TRANSLATE) or document extraction (PARSE_DOCUMENT). COMPLETE allows developers and analysts to embed AI-driven generative capabilities directly within SQL pipelines or Snowpark processes, eliminating the need for external LLM services.

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Changing a Snowflake virtual warehouse from Medium to Large increases the compute resources allocated to that warehouse—such as CPU cores, memory, and I/O bandwidth. This process is calledscaling up, or vertical scaling. It is typically performed to improve performance for complex queries, large joins, or computationally heavy workloads.

Scaling down is the reverse—reducing warehouse size from Large to Medium or similar.

Scaling out refers to adding more clusters to a multi-cluster warehouse, enabling increased concurrency rather than increasing power per cluster.

Scaling in is the reverse of scaling out—reducing the number of active clusters.

Thus, increasing the size from Medium to Large is vertical scaling, also known as scaling up.

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When auto-resume is enabled, Snowflake automatically starts a suspended warehouse when a query or task is submitted. This ensures smooth operations without manual intervention.

It does not scale the warehouse, prevent suspension, or require manual restarting.

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Snowflake uses the SQL command CREATE WAREHOUSE warehouse_name to create a new virtual warehouse. Warehouses provide the compute resources for executing SQL queries, loading data, and performing transformations. When creating a warehouse, additional parameters may be specified, such as warehouse size, auto-suspend timeout, auto-resume, scaling policy, or multi-cluster mode. The options involving “CREATE VIRTUALWAREHOUSE” are invalid syntax in Snowflake. CREATE DATABASE and CREATE SCHEMA create storage containers, not compute resources. Therefore, CREATE WAREHOUSE is the only correct command for provisioning compute.

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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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Snowflake Marketplace serves as a centralized catalog where providers publishready-to-use datasets, models, and applicationsacross industries. It includes:

Public datasets, such as demographics, weather, healthcare statistics, and government data.

Financial market datasets, including equities, commodities, macroeconomic indicators, and pricing feeds from major financial data vendors.

Third-party datasetssupplied by external organizations specializing in geospatial intelligence, retail analytics, marketing insights, healthcare, census data, and more.

“Proprietary datasets” are internal to organizations and typically not published on the public Marketplace unless the owner chooses to list them. Marketplace listings are curated and governed.

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Snowflake’s file format options allow granular control over CSV ingestion. The SKIP_HEADER = 1 parameter instructs Snowflake to ignore the first line of the file, typically containing column headers. This is the correct and official parameter within FILE FORMAT objects or inline COPY INTO options. Other options listed—such as SKIP_FILE_HEADER, IGNORE_HEADER, or HEADER_SKIP—are not valid Snowflake parameters. When SKIP_HEADER is applied, Snowflake begins reading data rows starting from line 2. This ensures proper alignment between file contents and table structure during ingestion. SKIP_HEADER can also be set to values greater than 1 if multiple header rows exist. This behavior is essential when loading structured data where the header row is not intended for table consumption.

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