SPS-C01 Exam Questions And Answers, Book SPS-C01 Free

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Snowflake SPS-C01 Exam Syllabus Topics:

SectionObjectives
Topic 1: User Defined Functions and Stored Procedures- Extending Snowpark with custom logic
  • 1. Stored procedures in Snowpark
    • 2. Python UDFs
      Topic 2: Performance Optimization and Best Practices- Efficient Snowpark execution
      • 1. Resource utilization tuning
        • 2. Pushdown optimization concepts
          Topic 3: Testing, Debugging, and Deployment- Production readiness
          • 1. Debugging Snowpark applications
            • 2. Deployment strategies
              Topic 4: Snowpark Fundamentals- Snowpark architecture and concepts
              • 1. Snowpark APIs and supported languages
                • 2. Snowflake execution model overview
                  Topic 5: DataFrame Operations and Data Processing- Data transformation workflows
                  • 1. Joins and window functions
                    • 2. Filtering, selecting, and aggregations
                      Topic 6: Data Engineering with Snowpark- Pipeline development
                      • 1. Batch processing workflows
                        • 2. Integration with Snowflake data pipelines

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                          Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q368-Q373):

                          NEW QUESTION # 368
                          You have two Snowpark DataFrames: 'employees_df with columns 'employee_id' (INTEGER), 'employee_name' (STRING), 'department_id' (INTEGER), and 'salaries_df' with columns 'employee_id' (INTEGER), "salary' (FLOAT), 'effective_date' (DATE). You need to create a new DataFrame that contains the employee's name, department, and the highest salary they have ever received. Assuming there can be multiple salary entries for the same employee with different 'effective date' values, which of the following Snowpark code snippets would correctly and efficiently solve this problem?

                          Answer: E

                          Explanation:
                          Option B is the most efficient solution. It first calculates the maximum salary for each employee in the DataFrame using 'groupBy' and 'max' , then joins this aggregated result with the 'employees_df to retrieve the employee's name and department. This approach avoids unnecessary data shuffling and minimizes the amount of data processed in the join. Option A performs the join before the aggregation, which can be less efficient. Options C and E use window functions, which are more complex and may not be as efficient for this simple aggregation. Option D uses a UDF and 'collect_list' , which can be very inefficient due to data transfer and UDF overhead.


                          NEW QUESTION # 369
                          You have a Snowpark DataFrame named 'sales df containing sales data for different products. The DataFrame includes columns product_id' (INTEGER), 'sale_date' (DATE), 'quantity' (INTEGER), and 'price' (FLOAT). You need to calculate the total revenue for each product on a monthly basis and store the result in a new DataFrame named Which of the following Snowpark code snippets will correctly achieve this, while maximizing performance and minimizing data shuffling?

                          Answer: E

                          Explanation:
                          Option B is the most efficient because 'date_trunc('MM', ...y truncates the date to the beginning of the month, allowing for proper grouping and aggregation without unnecessary string conversions or data shuffling. The 'date_trunc' function leverages Snowflake's internal date functions for optimal performance. Other options either use string representations of dates or date parts, which can lead to less efficient grouping.


                          NEW QUESTION # 370
                          You have a Snowpark Python UDF that performs sentiment analysis on customer reviews. The UDF relies on a pre-trained machine learning model stored as a file in a Snowflake stage. To enhance security, you want to create a secure UDF. Which of the following steps are necessary to achieve this?

                          Answer: A,E

                          Explanation:
                          Secure UDFs require explicit grants to access resources. Granting READ privilege on the stage to the UDF owner ensures access during definition. 'secure=True' makes the UDF secure. 'USAGE ON STAGE must be granted to the role executing the UDF to allow it to read from the stage at runtime. 'SNOWFLAKE.DATA GOVERNANCE' role doesn't automatically grant access, and 'EXECUTE AS CALLER is not directly related to granting access to the model file. 'context' is not a standard parameter for UDF definitions and does not manage security context directly.


                          NEW QUESTION # 371
                          You are developing a Snowpark application that processes high-volume event data stored in a Snowflake table named 'raw events'. The application aggregates data by session ID. You observe significant performance degradation during peak hours. Analyzing Snowflake query history reveals that the 'session_id' column has high cardinality and data skew. Which of the following strategies, or combination of strategies, would be MOST effective in optimizing the aggregation performance?

                          Answer: A,D

                          Explanation:
                          Using BUCKET_ID hint improves parallel processing and mitigates data skew in Snowpark. Pre-aggregation reduces the amount of data processed by the Snowpark application, thus improving performance. Increasing the warehouse size (A) might help but doesn't address data skew. UDFs (D) can introduce overhead if not optimized. GROUP BY alone (B) will not address the data skew problem


                          NEW QUESTION # 372
                          You have two Snowpark DataFrames, 'customers' and 'orders'. The 'customers' DataFrame has columns and 'customer name'. The 'orders' DataFrame has columns 'order id', 'customer id', and 'order amount'. You need to find all customers who have NOT placed any orders. Which of the following Snowpark set operations correctly implements this?

                          Answer: D

                          Explanation:
                          Option D is correct. It first selects the distinct 'customer_id' from both DataFrames. Then, it uses the 'minus' (or 'except_') set operation to find the difference between the customer IDs in the 'customers DataFrame and the customer IDs in the 'orders' DataFrame. This effectively returns the customer IDs of customers who have not placed any orders. The 'join' operation is not a set operation, and options B and C are not valid syntax for Snowpark. Option E is syntactically correct in Snowpark, and equivalent to Option D. However, since the question has to have one answer, Option D is kept.


                          NEW QUESTION # 373
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