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Snowflake SOL-C01 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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 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 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 (Q207-Q212):

NEW QUESTION # 207
A Snowflake environment hosts two distinct workloads: daily ETL processes that require significant compute power and ad-hoc analytical queries from business users. You want to optimize cost and performance for each workload. Which of the following virtual warehouse configurations would be the MOST effective approach?

Answer: D,E

Explanation:
Using separate warehouses allows you to size each warehouse appropriately for its specific workload. A larger warehouse can handle the ETL processing, while a smaller warehouse can serve ad-hoc queries. Configuring resource monitors adds another layer of cost control. Option A can lead to contention and inefficient resource utilization. Option C will likely result in poor performance for ETL. Option D wastes credits by keeping the large warehouse running continuously.


NEW QUESTION # 208
A data engineer needs to create a table in Snowflake to store JSON data from an external API.
The API returns a nested JSON structure that is frequently updated with new fields. Which data type is MOST suitable for storing this JSON data, and what are the key considerations for querying it efficiently?

Answer: B

Explanation:
VARIANT is the most suitable data type for storing semi-structured data like JSON in Snowflake.
It automatically infers the schema and allows for flexible storage of evolving JSON structures. Dot notation and the FLATTEN table function enable efficient querying of nested fields. While storage might be larger than VARCHAR due to internal metadata, the ease of use and query performance benefits outweigh this cost in many scenarios. Other options are less suitable because VARCHAR requires string manipulation, OBJECT needs a predefined schema, ARRAY requires an array structure, and BINARY needs custom conversion.


NEW QUESTION # 209
Which option is available in Snowsight for loading data from a local file?

Answer: A

Explanation:
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.


NEW QUESTION # 210
A Snowflake account uses separate virtual warehouses for different workloads: 'REPORTING WI-
4' for reporting queries and
'TRANSFORM WH' for data transformation jobs. A user, Alice, is responsible for running both types of workloads. Alice is experiencing performance issues with reporting queries when the transformation jobs are running concurrently. Both warehouses are currently configured with the
'STANDARD' size. What are the recommended strategies to improve the performance of reporting queries without significantly impacting the performance of transformation jobs?

Answer: A

Explanation:
Enabling a multi-cluster warehouse for 'REPORTING_WH' with a 'STANDARD' scaling policy allows Snowflake to automatically scale up the number of clusters to handle the increased workload from concurrent reporting queries. The Query Acceleration Service (QAS) will try to reduce the resource consumption of reporting queries. Implementing Resource Monitors for
`TRANSFORM WHS will prevent it from consuming excessive credits and impacting the performance of 'REPORTING_WH' during concurrent execution.


NEW QUESTION # 211
You are tasked with securing sensitive data in a table called `CUSTOMER DATA'. You want to grant the 'SELECT privilege on this table to a role called 'ANALYST ROLE, but prevent them from seeing the 'CREDIT CARD NUMBER column, which contains personally identifiable information (PII). Which of the following approaches are valid in Snowflake to achieve this?

Answer: A,B,D

Explanation:
Option A is valid: Creating a view and granting 'SELECT on the view allows you to control which columns are visible to the 'ANALYST ROLE. Option B is valid: Dynamic data masking allows you to mask the sensitive column for unauthorized users (in this case, 'ANALYST_ROLE') while allowing authorized users to see the unmasked data using the 'UNMASK' privilege. Option C is invalid: You cannot revoke privileges on a specific column; privileges are granted and revoked at the table level. Option D is incorrect: Row Access Policies filter rows, not columns. Option E is valid: Secure views are the most secure way to filter the specific data.


NEW QUESTION # 212
......

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