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Where is unstructured data stored in Snowflake?
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.
Which of the following Snowflake functions is used to generate pre-signed URLs for accessing files in a stage?
GET_PRESIGNED_URL() is the official Snowflake function used to create atemporary, secure, time-limited pre-signed URL, which provides controlled-access retrieval of a file stored in a Snowflake internal or external stage. Pre-signed URLs are essential for secure file sharing without exposing credentials or granting direct stage access to external users. The function accepts the stage name and file path and optionally allows specifying an expiration period. GET_STAGE_LOCATION() retrieves metadata about where a stage points, not access URLs. BUILD_STAGE_FILE_URL() constructs a basic URL but does not sign it, meaning it cannot authorize secure download access. GET_RELATIVE_PATH() provides path-only details, not a secure download link. GET_PRESIGNED_URL ensures that external consumers can access staged files safely, making it an important feature for secure distribution workflows, data export pipelines, partner integrations, and temporary file sharing scenarios.
How can users filter queries in the Query History view in Snowsight?
Snowsight's Query History view allows filtering queries bystatus,date range, andexecuting user. Additional filters include SQL text search, warehouse, duration, statement type, session ID, query ID, query tag, parameterized query hash, and whether a query was generated by a client or task. These filters help users identify long-running, failed, queued, or specific types of queries for troubleshooting and performance optimization. Network or hardware configurations have no role in Query History filtering.
When a virtual warehouse is scaled up (e.g., from X-Small to Small), what is the primary impact?
Scaling up a warehouse increases compute resources (CPU, memory), improving performance on heavy workloads: large joins, transformations, and long-running analytical queries.
It does not increase storage, reduce cost, or guarantee higher concurrency (multi-cluster handles concurrency).
What are computer language considerations when using Snowflake interfaces? (Select TWO).
Snowflake Notebooks support both SQL and Python as executable cell types. This means that users can create notebook cells written in Python for programmatic data processing and modeling, and in SQL for declarative querying and transformation. Because of this, the statements ''Notebook cells can be written in Python'' and ''Notebook cells can be written in SQL'' are both correct.
Snowflake databases do not natively execute queries written in Scala; Scala is supported via Snowpark APIs for application code, not as a direct query language. Worksheets in Snowsight are primarily SQL-based and also support procedural constructs (e.g., Snowflake Scripting), so the phrase ''only in SQL'' is not a precise or complete characterization. Dashboards in Snowsight are created using SQL-backed visualizations and built-in UI components; JavaScript is not a supported authoring language inside the native dashboarding layer. Therefore, options B, D, and E are not correct in this context.
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4 domains from the Snowflake SOL-C01 exam outline, with approximate weightings. Every sample question above is tagged with the domain it comes from
Understand Snowflake's core features including elastic storage and compute, and learn to navigate key user interfaces like Snowsight, Notebooks, and Worksheets. Work with the Snowflake object hierarchy including databases, schemas, tables, and views to organize and structure your data effectively.
Define and manage role-based access control including role hierarchies and privilege assignment. Use database objects, schemas, and the INFORMATION_SCHEMA to understand context and manage access, then transfer ownership as needed using basic SQL commands.
Manage both structured and semi-structured data using stages and the COPY INTO command. Design and manage virtual warehouses including sizing and scaling decisions, then use multiple methods to load and query data including Snowsight and INSERT statements.
Implement continuous data protection using Time Travel and cloning to recover and manage data versions. Enable data sharing through Snowflake Marketplace and Data Exchange to share datasets securely with internal teams or external partners.
Common questions about the exam itself
