Free Snowflake SOL-C01 Practice Exam with Questions & Answers | Set: 4

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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Scaling in/outrefers tohorizontal scaling, which is available only inmulti-cluster warehouses. When Snowflake scalesout, additional compute clusters are added automatically to handle high concurrency, preventing query queuing. When load decreases, Snowflake scalesin, reducing cluster count to save compute credits.

This differs fromscaling up/down, which refers to changing warehouse size (X-Small → Small → Medium). Vertical scaling increases compute resources for a single cluster but does not improve concurrency for simultaneous workloads.

Scaling in/out is valuable for BI dashboards, workloads with many simultaneous users, peak-hour query loads, and applications requiring consistent performance under high concurrency.

Changing regions is an account-level configuration and unrelated to warehouse scaling.

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TheCLASSIFY_TEXTfunction performscategory assignmentfor free-form text. Users provide a list of predefined labels, and the function predicts the most appropriate category using a Snowflake-optimized LLM. It is ideal for automatically tagging customer feedback, routing support tickets, labeling product reviews, classifying email content, and segmenting operational logs.

The function outputs both the predicted label and an associated confidence score, enabling downstream filtering, dashboarding, and automated processing.

Other options belong to different Cortex functions:

TRANSLATEhandles multilingual translation.

COMPLETEgenerates natural language completions.

PARSE_DOCUMENTextracts structured information from documents.

Thus, CLASSIFY_TEXT is task-specific and optimized specifically for classification workloads.

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Snowflake Notebooks provide an integrated, interactive development environment that enables users to write SQL and Python code, visualize results, and document analysis—all in the same workspace. They support data visualizations, charts, tables, and Streamlit-based interactions. Notebooks run entirely within Snowsight and require no manual hardware configuration. They are not CLI-based and support much richer displays than plain text. This integration streamlines the entire data exploration, transformation, and modeling workflow.

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A single VARIANT value in Snowflake has a maximum size limit of16 MB(uncompressed). This limit applies to semi-structured data stored in VARIANT, such as JSON, XML, Avro, and Parquet objects.

This limit ensures efficient storage, query performance, and micro-partitioning. Exceeding the size results in load or processing errors. For ingestion, when loading large JSON files, it’s recommended to break them into multiple objects or use JSON Lines formatting.

Snowflake has introduced an optional behavior change bundle (2025_03), increasing the limit to128 MBfor accounts that enable it, but the standard default remains 16 MB.

Thus, under default behavior,16MBis the correct maximum value size.

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A Snowflake database is a top-level container that can include schemas, and within those schemas it can contain objects such astables,views,stages,functions,procedures, anddatabase roles. However,global account-level roles(such as SYSADMIN, SECURITYADMIN, PUBLIC, or custom account roles) do not reside inside a database. These roles are created and managed at the Snowflake account level, not within any specific database. In contrast,database rolesare scoped within a specific database and can exist inside it. Views and tables are standard database objects. Therefore, the only option that a Snowflake database cannot contain is an account-levelRole.

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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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Snowflake processes queries usingMassively Parallel Processing (MPP)compute clusters, deployed as virtual warehouses. Each warehouse consists of multiple compute nodes working in parallel to execute queries efficiently. When a query is submitted, Snowflake distributes tasks across nodes, processes data subsets concurrently, and aggregates results. This architecture enables high performance, scalability, and the ability to handle complex analytical workloads. While Snowflake does incorporate elements of shared-disk storage, query execution itself depends on MPP compute clusters. Options such as third-party connectors or storage optimization do not represent the core query processing mechanism.

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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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Snowflake supports two official stage types:

• Internal Stages – User stages, table stages, and named internal stages stored inside Snowflake.

• External Stages – Refer to cloud storage (S3, Azure Blob, GCS) using integration credentials.

Managed, permanent, and temporary stages are not official Snowflake classifications.

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