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SOL-C01 Valid Test Bootcamp - Test SOL-C01 Guide Online
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Snowflake SOL-C01 Exam Syllabus Topics:| Topic | Details | | Topic 1 | - 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.
| | Topic 2 | - 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 3 | - 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 4 | - 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.
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Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q143-Q148):NEW QUESTION # 143
You have a table 'CUSTOMER REVIEWS' with a column 'REVIEW TEXT containing unstructured customer feedback. You want to use CLASSIFY TEXT to categorize these reviews into predefined categories like 'Product Quality', 'Customer Service', and 'Shipping'. You plan to create a materialized view to store the classifications for faster querying. Which of the following SQL statements is the most efficient and correct way to achieve this using CLASSIFY TEXT, assuming the function has already been granted usage privileges?
- A. CREATE MATERIALIZED VIEW CLASSIFIED_REVIEWS AS SELECT REVIEW_TEXT, Quality",
"Customer Service", "Shipping"]')) AS CATEGORY FROM CUSTOMER REVIEWS; - B. CREATE MATERIALIZED VIEW CLASSIFIED_REVIEWS AS SELECT REVIEW_TEXT, ['Product Quality', 'Customer Service', 'Shipping')) AS CATEGORY FROM CUSTOMER REVIEWS;
- C. CREATE MATERIALIZED VIEW CLASSIFIED_REVIEWS AS SELECT REVIEW_TEXT, OBJECT CONSTRUCT('categories', ARRAY CONSTRUCT('Product Quality', 'Customer Service',
'Shipping'))) AS CATEGORY FROM CUSTOMER REVIEWS; - D. CREATE MATERIALIZED VIEW CLASSIFIED_REVIEWS AS SELECT REVIEW_TEXT, Quality',
'Customer Service', 'Shipping')) AS CATEGORY FROM CUSTOMER REVIEWS; - E. CREATE MATERIALIZED VIEW CLASSIFIED_REVIEWS AS SELECT REVIEW_TEXT, 'Product Quality', 'Customer Service', 'Shipping') AS CATEGORY FROM CUSTOMER REVIEWS;
Answer: D
Explanation:
The correct syntax for providing categories to CLASSIFY TEXT is using ARRAY CONSTRUCT.
Option B is the only one that correctly uses ARRAY_CONSTRUCT to pass the categories as an array. Options A, C, D and E all use incorrect syntax and will result in errors. Materialized views are suitable for storing the results of CLASSIFY TEXT for performance reasons.
NEW QUESTION # 144
A data warehouse architect is designing a Snowflake environment for a large retail company. The company has diverse data workloads, including batch data ingestion, real-time analytics, and machine learning model training. The architect needs to choose the appropriate Snowflake edition and configure the virtual warehouse sizes to optimize cost and performance. Considering the diverse workloads, which combination of Snowflake edition and virtual warehouse strategy would be most appropriate?
- A. Snowflake Standard Edition with a single X-Large virtual warehouse shared across all workloads.
- B. Snowflake Business Critical Edition with a single Medium virtual warehouse that auto-scales based on the workload demand.
- C. Snowflake Enterprise Edition with separate virtual warehouses of varying sizes (Small to X-Large) for each workload (batch ingestion, real-time analytics, and ML model training). Use resource monitors to control costs.
- D. Snowflake Standard Edition with separate Small virtual warehouses for batch ingestion and reporting, but use Amazon SageMaker for ML training.
- E. Snowflake Virtual Private Snowflake (VPS) with a dedicated virtual warehouse for each department within the retail company.
Answer: C
Explanation:
Option B is the most appropriate. Enterprise Edition provides the features necessary for diverse workloads (such as materialized views and advanced security), and using separate virtual warehouses allows for workload isolation and optimized resource allocation. Resource monitors can control costs. Standard Edition (A and E) lacks the features required for advanced workloads.
Business Critical Edition (C) is overkill unless specific security or compliance requirements mandate it. VPS (D) is suitable for highly regulated industries but may be more expensive than necessary for a retail company. Using external services such as SageMaker (E) defeats the purpose of leveraging Snowflake for all workloads.
NEW QUESTION # 145
A data analyst needs to query a table named 'ORDERS' which contains sensitive customer information, including email addresses. They only need to access order IDs and order dates for their analysis, but they are mistakenly seeing all columns. You, as the Snowflake administrator, need to restrict their access to only the necessary columns without creating a separate table or view. What is the MOST secure and efficient way to achieve this in Snowflake using Row Access Policies and Column Masking Policies? Assume the role 'ANALYST' is already defined.
- A. Create a dynamic data masking policy for the `EMAIL' column and a row access policy that only returns rows based on a user attribute for 'ANALYST'. Grant SELECT privilege to the ANALYST role on the 'ORDERS' table.
- B. Apply a Column Masking Policy to the 'EMAIL' column to mask the data (e.g., replace it with NULL) when accessed by the ANALYST role, and then grant the ANALYST role SELECT access to the "ORDERS' table.
- C. Create a view on the 'ORDERS' table that selects only the 'ORDER_ID and 'ORDER_DATE columns, and grant the ANALYST role SELECT access to the view.
- D. Apply a Row Access Policy that allows only rows with a specific order date, and then grant the ANALYST role SELECT access to the 'ORDERS' table.
- E. Grant the ANALYST role SELECT access to the 'ORDERS' table, and then use 'ALTER TABLE to drop all columns except 'ORDER ID and 'ORDER DATE.
Answer: B
Explanation:
Option D is the best approach because it directly addresses the requirement of restricting access to sensitive data. It applies a Column Masking Policy to the "EMAIL' column, preventing the analyst from viewing the actual email addresses while still allowing them to access the
'ORDER_ID and 'ORDER DATE columns. Option A creates a view, which is a valid approach, but using masking policies provides more granular control and auditability. Option B is destructive and unacceptable. Option C restricts rows, which is not what the question asks for. Option E is an overkill since row access policies are not needed in this case.
NEW QUESTION # 146
What is a key benefit of using Snowsight for loading data into Snowflake?
- A. Limited to specific file formats
- B. Manual data storage configuration
- C. Simplified and intuitive data loading process
- D. Requires external tools for data loading
Answer: C
Explanation:
Snowsight provides an intuitive UI-based workflow for loading data, enabling drag-and-drop file upload, schema inference, previewing records, automatic table creation, and loading into existing tables. Users can load CSV, JSON, Parquet, AVRO, ORC, and other supported formats without writing COPY INTO commands manually.
Snowsight also generates the equivalent SQL code, helping users understand and reuse ingestion scripts in pipelines. It eliminates the need for external tools such as SnowSQL CLI or custom ETL scripts.
Incorrect statements:
* External tools are not required.
* Manual storage configuration is unnecessary; Snowflake abstracts all storage.
* Snowsight supports a wide variety of formats, not limited ones.
Thus, the key advantage is asimple, guided data-loading workflow, especially useful for new Snowflake users.
NEW QUESTION # 147
How do databases and schemas fit into Snowflake's hierarchy? (Choose any 2 options)
- A. A schema can contain multiple databases
- B. A table can contain multiple schemas
- C. A database can contain multiple schemas
- D. A schema can contain multiple tables and views
Answer: C,D
Explanation:
Snowflake's object hierarchy begins with theaccount, which contains one or moredatabases. Each database can contain multipleschemas, and each schema contains objects such astables,views,stages, and functions.
The schema is a logical boundary for grouping related database objects. A schema cannot contain databases, and tables cannot contain schemas. Instead, tables and views are contained within schemas. Therefore, the two correct hierarchical relationships are that a database can contain multiple schemas, and a schema can contain multiple tables and views.
NEW QUESTION # 148
......
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