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Snowflake SPS-C01 Exam Syllabus Topics:

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

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                          Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q169-Q174):

                          NEW QUESTION # 169
                          You are developing a Snowpark Python application that processes streaming data using a dynamic table. The application is experiencing frequent 'net.snowflake.client.jdbc.SnowflakeSQLException: SQL compilation error: Unsupported feature 'Streaming Dynamic Table'. ' errors, even though dynamic tables are enabled in your Snowflake account and the user has the necessary privileges. Which of the following are potential causes and solutions for this error? (Select TWO)

                          Answer: C,D

                          Explanation:
                          Options A and C are correct. Streaming Dynamic Tables require specific warehouse configuration and Snowpark client version. Option A: The 'STREAMING DYNAMIC TABLES' parameter must be enabled on the warehouse. Option C: An outdated Snowpark client might not support the streaming dynamic table feature. Option B might cause a different SQL compilation error related to syntax, but not the specific 'Unsupported feature' error. Option D is related to managed tasks, not dynamic tables directly. Option E would affect performance and potential data staleness, but not the 'Unsupported feature' error. Therefore, the correct answers are A and C.


                          NEW QUESTION # 170
                          You have a Snowpark DataFrame with columns 'product_id', 'customer_id', and 'sale_amount'. Some values are negative, indicating returns, and others are null. You need to replace negative values with 0 and fill null values with the average 'sale_amount' for each 'product_id'. Which of the following approaches is the MOST efficient and correct way to achieve this using Snowpark?

                          Answer: D

                          Explanation:
                          Option E first replaces negative values with 0. Then, it calculates the average sales amount per product and joins it back to the original DataFrame. Finally, it fills null values with the calculated average sales amount and drops the temporary column. This is efficient because it utilizes Snowpark's DataFrame operations. other options does not handle nulls or gives errors.


                          NEW QUESTION # 171
                          You have a Snowpark DataFrame named containing daily sales transactions. The DataFrame includes columns like 'transaction_id' , 'product id', 'sale_date', and 'sale_amount'. You need to perform the following transformations and persist the results: (1) Calculate the total sales amount for each product on a daily basis. (2) Store the aggregated results into a new table named , partitioned by 'sale_date'. (3) Ensure that if the table already exists, the new data is appended to the existing table. Which of the following code blocks achieve these requirements in the most efficient and correct manner?

                          Answer: A

                          Explanation:
                          Option A is the most efficient and correct. It first aggregates the data as required, then uses 'mode('append')' to add new data without overwriting existing data, and for partitioning. Option B will overwrite existing data. Option C is equivalent to option A, but is a less common coding style. Option D renames the default aggregate column name which is less readable but also works correctly. Option E, 'mergeSchema' isn't a valid option to be passed.


                          NEW QUESTION # 172
                          You are developing a Snowpark application that uses a Python UDF to perform geocoding operations. This UDF relies on a third-party geocoding library and a large dataset of geographical data stored in a file named 'geodata.db'. The UDF needs to be operationalized with minimal latency. Which of the following strategies will result in the FASTEST execution of the UDF and optimal resource utilization?

                          Answer: E

                          Explanation:
                          Option E is the most efficient strategy. Packaging the library and data file in a ZIP, referencing it with 'imports' , and using a global variable with caching within the UDF minimizes latency by loading the data only once per worker. It also benefits from utilizing the parallel processing capabilities of Snowpark. Using Java UDF's (C) is less efficient, unless it is highly optimized since java conversion can happen and adds overhead . Relying on external geocoding services (D) introduces network latency and is not ideal for performance. While a custom Anaconda channel (B) can simplify dependency management, it does not address the issue of loading the large 'geodata.db' file efficiently. Option A addresses the dependency managment but performance is not addressed.


                          NEW QUESTION # 173
                          Given a Snowpark DataFrame 'employees_df with columns 'employee_id' , 'department' , and 'salary' , and a second Snowpark DataFrame 'departments_df with columns 'department_id' and which of the following Snowpark code snippets correctly performs a join to retrieve employee information along with their department name, filtering for employees with salaries greater than $60,000, and then orders the result by department name?

                          Answer: B

                          Explanation:
                          Explanation:Option E first filters the 'employees_df for salaries greater than $60,000, then joins with 'departments_df using the appropriate join condition (employee.department = department.department_id), and finally orders the result by department name. Using instead of and instead of also acceptable syntax. It's more efficient to filter before joining, so E is better than D, and more importantly E and D work correctly. The other options will throw an error.


                          NEW QUESTION # 174
                          ......

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