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[General] SOL-C01 Exam | Valid SOL-C01 Test Pattern

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【General】 SOL-C01 Exam | Valid SOL-C01 Test Pattern

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Snowflake SOL-C01 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Data Protection and Data Sharing: This domain addresses continuous data protection through Time Travel and cloning, plus data collaboration capabilities via Snowflake Marketplace and private Data Exchange sharing.
Topic 2
  • Identity and Data Access Management: This domain focuses on Role-Based Access Control (RBAC) including role hierarchies and privileges, along with basic database administration tasks like creating objects, transferring ownership, and executing fundamental SQL commands.
Topic 3
  • Data Loading and Virtual Warehouses: This domain covers loading structured, semi-structured, and unstructured data using stages and various methods, virtual warehouse configurations and scaling strategies, and Snowflake Cortex LLM functions for AI-powered operations.
Topic 4
  • Interacting with Snowflake and the Architecture: This domain covers Snowflake's elastic architecture, key user interfaces like Snowsight and Notebooks, and the object hierarchy including databases, schemas, tables, and views with practical navigation and code execution skills.

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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q174-Q179):NEW QUESTION # 174
Which function in Snowflake Cortex LLM are Task Specific Function? (Select two)
  • A. CLASSIFY_TEXT / AI_CLASSIFY
  • B. PARSE_DOCUMENT
  • C. COUNT_TOKENS
  • D. TRANSLATE
Answer: A,B,D
Explanation:
Snowflake's Cortex LLM includestask-specific functions, meaning each performs a well-defined AI operation with predictable outputs. Examples include:
* TRANSLATE- Converts text between languages; deterministic and domain-independent.
* CLASSIFY_TEXT / AI_CLASSIFY- Assigns text to predefined categories, ideal for sentiment, topics, or routing tasks.
* PARSE_DOCUMENT- Extracts structured information from documents (PDFs, invoices, receipts, contracts) including layout-aware content.
These functions are optimized for reliability, reproducibility, and governance, making them suitable for production pipelines.
COUNT_TOKENSis not task-specific-it's a utility function used to estimate LLM token usage rather than perform a primary AI task.
Thus, TRANSLATE, CLASSIFY_TEXT, and PARSE_DOCUMENT are the correct task-specific functions.

NEW QUESTION # 175
What does a "green" cell execution status indicate in Snowflake Notebooks?
  • A. Execution was canceled
  • B. Execution is pending
  • C. Execution succeeded
  • D. Execution failed
Answer: C

NEW QUESTION # 176
A data engineer needs to create a new database named `SALES DATA in Snowflake. They want to ensure that only users with the 'ACCOUNTADMIN' role can initially modify the database. After creation, they will grant specific privileges to other roles. Which of the following SQL statements is the most appropriate way to create the database with these requirements?
  • A. CREATE DATABASE SALES DATA WITH DATA RETENTION TIME IN DAYS-I;
  • B. CREATE DATABASE SALES DATA WITH OWNER = 'ACCOUNTADMIN';
  • C. CREATE DATABASE SALES DATA;
  • D. CREATE DATABASE SALES DATA COMMENT = 'Sales Data Database';
  • E. CREATE OR REPLACE DATABASE SALES DATA;
Answer: C
Explanation:
Option A creates the database. Initially, 'ACCOUNTADMIN' role has ownership and thus can modify it. Options B, C, and E are valid syntax but do not explicitly restrict initial modification access to only `ACCOUNTADMIN' post-creation. Option D is invalid syntax as OWNER cannot be specified during database creation. Permissions are managed separately using GRANT statements after creation.

NEW QUESTION # 177
Which of the following statements are true regarding considerations when working with VARIANT data types in Snowflake? (Select all that apply)
  • A. You can use the operator to cast data extracted from a VARIANT column to a specific data type.
  • B. Queries on VARIANT columns can be slower than queries on strongly-typed columns because Snowflake needs to infer the data type at runtime.
  • C. Data stored in VARIANT columns always consumes less storage space than the equivalent data stored in strongly-typed columns.
  • D. Snowflake automatically indexes VARIANT columns, eliminating the need for manual index creation.
  • E. VARIANT columns can store data with different schema within the same column.
Answer: A,B,E
Explanation:
Option A is correct: Querying VARIANT columns involves runtime type inference, leading to potential performance overhead. Option C is correct: VARIANT columns are designed to store data with flexible schema. Option D is correct: The operator is used for type casting when extracting data from VARIANT columns. Option B is incorrect: Snowflake does not automatically index VARIANT columns. Option E is incorrect: VARIANT columns can sometimes consume more storage space due to the metadata required to describe the data structure.

NEW QUESTION # 178
What is a primary function of a view in Snowflake?
  • A. Managing user authentication
  • B. Storing raw data
  • C. Providing a virtual table based on the result set of a query
  • D. Executing network configurations
Answer: C
Explanation:
A view in Snowflake represents avirtual tablewhose contents are defined by a stored SQL query. Views allow users to encapsulate transformation logic, simplify complex joins, enforce column-level security, and provide curated datasets for downstream consumers.
Views donotstore data themselves; the underlying tables store the data, and Snowflake executes the view's query each time the view is referenced. This makes views ideal for abstraction layers, semantic modeling, and separating compute costs across user groups.
Incorrect options:
* Snowflake views do not store raw data.
* Views have no role in authentication or network configuration.
Views help streamline business logic and control data access efficiently.

NEW QUESTION # 179
......
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