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[General] Knowledge SOL-C01 Points & New SOL-C01 Test Fee

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【General】 Knowledge SOL-C01 Points & New SOL-C01 Test Fee

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Snowflake SOL-C01 Exam Syllabus Topics:
TopicDetails
Topic 1
  • 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 2
  • 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 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.

Snowflake Certified SnowPro Associate - Platform Certification Sample Questions (Q23-Q28):NEW QUESTION # 23
You have a table 'PRODUCT_PRICES' defined as 'CREATE TABLE PRODUCT_PRICES (PRODUCT_ID INT, PRICE LAST UPDATED TIMESTAMP NTZ)'. You want to insert new prices for some products, but only if the new price is different from the existing price. If the price is the same, you want to update the 'LAST UPDATED' timestamp. Which of the following approaches would be the most efficient in Snowflake to achieve this?
  • A. Create a new table with all the product IDs that need to be updated and use 'INSERT OVERWRITE to replace original table.
  • B. Use `MERGE statement to update the 'LAST UPDATED timestamp and insert new records if a record for the 'PRODUCT ID does not exist.
  • C. First 'INSERT all the new prices into a temporary table, then use a 'JOIN' with the original table to identify the rows that need to be updated or inserted.
  • D. Perform a 'SELECT statement for each 'PRODUCT_ID to check the existing price, then either
    'INSERT or 'UPDATE accordingly.
  • E. Use a stored procedure that iterates through the new data and performs 'INSERT or 'UPDATE statements based on the existing price.
Answer: B
Explanation:
Option C is the most efficient because the 'MERGE statement is specifically designed for scenarios where you need to conditionally insert or update data based on a join condition. It avoids the overhead of multiple `SELECT statements or a stored procedure iterating through the data. A and B perform row-by- row operations which is slow in Snowflake. Although D is valid, it requires you to create temporary tables, populate and then join against the original table, which takes more time and code. Option E overwrites the whole table which is not needed since we only need to update records if a record for the 'PRODUCT_ID' does not exist.

NEW QUESTION # 24
A data engineering team is experiencing performance issues with their nightly ETL pipeline in Snowflake. The pipeline involves complex transformations on a large dataset (5TB) and is executed within a single Snowflake virtual warehouse (size: Large). The team notices that the warehouse is frequently hitting resource limits (CPU and Memory) during peak processing times, even though the overall execution time is only 2 hours. Which of the following strategies would BEST address the performance bottleneck and optimize resource utilization, considering cost- effectiveness?
  • A. Break down the ETL pipeline into smaller, independent tasks and use multiple smaller virtual warehouses (size: Medium) to execute them in parallel. This utilizes Snowflake's multi-cluster architecture and distributes the workload.
  • B. Implement scaling policies for the virtual warehouse. Configure it to automatically scale up to X- Large during the ETL pipeline's execution and then scale back down to Large when the pipeline completes.
  • C. Migrate the entire ETL pipeline to a different data processing platform like Apache Spark, as Snowflake is not suitable for complex transformations on large datasets.
  • D. Optimize the SQL queries within the ETL pipeline by identifying and rewriting inefficient queries, adding appropriate indexes, and leveraging Snowflake's query optimization features.
  • E. Upgrade the virtual warehouse size to X-Large to provide more CPU and memory resources. This directly addresses the resource contention.
Answer: A,D
Explanation:
Option C (Breaking down the ETL pipeline) is a strong choice as it leverages Snowflake's multi- cluster architecture for parallel processing, improving performance and resource utilization.
Option D (Optimizing SQL queries) is also crucial. Inefficient queries can significantly impact performance. Options A and B address the problem, but not as efficiently as C and D. While upgrading the warehouse (A) might provide temporary relief, it doesn't fundamentally address inefficiencies. Auto-scaling (B) is good, but splitting the load provides true parallelism. Option E is an extreme measure and likely unnecessary with proper optimization.

NEW QUESTION # 25
You are tasked with transferring ownership of a database named 'FINANCIAL DATA' from the
'ACCOUNTADMIN' role to a custom role named 'DATA GOVERNANCE'. After the transfer, the
'DATA GOVERNANCE' role should have full control over the database. Which of the following steps must be performed to ensure a successful ownership transfer?
  • A. Grant the 'OWNERSHIP' privilege on the database to the 'DATA_GOVERNANCE role using
    'GRANT OWNERSHIP ON DATABASE FINANCIAL_DATA TO ROLE DATA_GOVERNANCE;'
    and ensure the role is active. No additional privileges are required.
  • B. Grant the 'OWNERSHIP' privilege on the database to the 'DATA_GOVERNANCE role using
    "GRANT OWNERSHIP ON DATABASE FINANCIAL_DATA TO ROLE DATA GOVERNANCE$.
    The DATA GOVERNANCE role must be the current role for the user performing the operation.
  • C. Execute the command: 'GRANT OWNERSHIP ON DATABASE FINANCIAL DATA TO ROLE DATA GOVERNANCE;'
  • D. Grant the OWNERSHIP' privilege on the database to the 'DATA_GOVERNANCE role using
    'GRANT OWNERSHIP ON DATABASE FINANCIAL_DATA TO ROLE DATA GOVERNANCE$.
    Then, explicitly grant all other required privileges (e.g., USAGE, CREATE) on the database to the
    `DATA GOVERNANCE role.
  • E. Grant the 'OWNERSHIP privilege on the database to the 'DATA_GOVERNANCE role using
    'GRANT OWNERSHIP ON DATABASE FINANCIAL_DATA TO _GOVERNANCE$. The user
    executing this command must have the 'ACCOUNTADMIN' role, and the 'DATA_GOVERNANCE' role must already have ROLE DATA the 'USAGE privilege on the database and all schemas within it.
Answer: A
Explanation:
The `GRANT OWNERSHIP command automatically revokes the OWNERSHIP privilege from the previous owner and grants it to the new owner. The user executing must have the necessary privileges to grant OWNERSHIP. The role receiving ownership needs no special privileges beforehand, as it is gaining complete control. Option C suggests 'USAGE privilege must be granted beforehand, which is not necessarily needed to transfer Ownership. Option D implies other privileges must be explictly granted but 'OWNERSHIP' already grants all permissions.
Option E requires that the DATA_GOVERNANCE role must be current role for user which is unnecessary.

NEW QUESTION # 26
You have a Snowflake database named 'SALES DB' containing schemas 'RAW' and
'TRANSFORMED'. The 'RAW' schema holds raw data ingested from various sources, and the
'TRANSFORMED' schema contains cleansed and transformed data ready for reporting. You need to grant the 'DATA ANALYST' role the ability to read all tables in the 'TRANSFORMED' schema but prevent them from modifying any objects. What is the MOST efficient and secure way to achieve this?
  • A. GRANT USAGE ON DATABASE SALES DB TO ROLE DATA ANALYST; GRANT SELECT ON FUTURE TABLES IN SCHEMA SALES DB.TRANSFORMED TO ROLE DATA ANALYST;
  • B. GRANT ALL PRIVILEGES ON SCHEMA SALES DB.TRANSFORMED TO ROLE DATA
    ANALYST;
  • C. GRANT SELECT ON SCHEMA SALES DB.TRANSFORMED TO ROLE DATA ANALYST;
  • D. GRANT SELECT ON ALL TABLES IN SCHEMA SALES DB.TRANSFORMED TO ROLE DATA ANALYST;
  • E. GRANT USAGE ON DATABASE SALES DB TO ROLE DATA ANALYST; GRANT USAGE ON SCHEMA SALES DB.TRANSFORMED TO ROLE DATA ANALYST; GRANT SELECT ON FUTURE TABLES IN SCHEMA SALES DB.TRANSFORMED TO ROLE DATA ANALYST;
Answer: E
Explanation:
The most efficient and secure approach is to grant USAGE on the DATABASE and SCHEMA, and SELECT on FUTURE TABLES (C). USAGE privilege on the database allows the role to access objects within the database. USAGE on the schema allows access to the schema.
Granting SELECT on FUTURE TABLES ensures that any new tables created in the TRANSFORMED schema will automatically inherit the SELECT privilege for the DATA ANALYST role. Option A only grants SELECT on existing tables, not future ones. Option D grants ALL PRIVILEGES, which is too permissive. Option E is insufficient as it doesn't grant required privileges on Database and Schema. Option B is partially correct but misses granting usage on SCHEMA.

NEW QUESTION # 27
What is the primary benefit of the Snowflake data cloud?
  • A. It provides direct access to underlying infrastructure.
  • B. It eliminates the need for data governance.
  • C. It enables organizations to unite and share their data.
  • D. It replaces traditional data warehouses with on-premises solutions.
Answer: C
Explanation:
The Snowflake Data Cloud allows organizations toseamlessly share, access, and collaborate on dataacross departments and external partners, without copying or moving data. Through secure data sharing, listings, and data clean rooms, Snowflake eliminates data silos and dramatically improves data collaboration.
It does not eliminate the need for governance-Snowflake enhances governance via RBAC, masking policies, and centralized controls. It does not provide access to underlying cloud infrastructure; Snowflake abstracts that. It is not an on-premises solution; Snowflake is fully cloud-native.
Thus, the primary benefit is unifying and securely sharing data across the ecosystem.

NEW QUESTION # 28
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