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

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                          Snowflake Certified SnowPro Specialty - Snowpark Sample Questions (Q24-Q29):

                          NEW QUESTION # 24
                          You have a DataFrame 'df containing user profile data'. A column named "profile" stores JSON objects with potentially missing fields. These objects might include 'name', 'age', 'location', and 'preferences'. You need to extract the user's name and age. If 'age' is missing, you want to default to 0. Furthermore, you want to filter out any rows where the 'location' field is an empty string. Which combination of Snowpark code snippets will achieve this? (Select all that apply)

                          Answer: C,D,E

                          Explanation:
                          Options A, B and C correctly address the requirements. They extract the name and handle the missing 'age' by defaulting to 0, and filter out rows with an empty string for 'location'. - Option A uses 'nvr to replace NULL 'age' values and ' to filter out empty locations. - Option B uses 'iff to achieve the same age default and sf.lit(")' for filtering. - Option C uses 'coalesce' to default 'age' and 'sf.lengtm to check if the location string has any characters. Option D will filter out all the rows where location is Null and option E uses zeroifnull function which is deprecated, and replaces age which is 0 if column is NULL. and option D wont filer empty string in location


                          NEW QUESTION # 25
                          Consider the following Snowpark Python code snippet:

                          Answer: B,E

                          Explanation:
                          ' will indeed overwrite the table if it exists. When a schema is not explicitly specified, Snowpark defaults to the schema of the user's current session for table creation. No explicit call to an action like 'collect()' or show()' is needed to trigger execution, as is an action itself. is a valid operation to persist DataFrames as tables in Snowpark. The table name does not strictly need to be fully qualified unless targeting a schema different from the user's default.


                          NEW QUESTION # 26
                          You are working with a Snowpark DataFrame containing employee data, including columns 'employee_id', 'first_name', 'last_name', 'salary', and 'department'. You need to perform the following transformations: 1. Concatenate 'first_name' and into a new column called separating them with a space. 2. Increase each employee's salary by a percentage based on their 'department'. Department 'Sales' gets a 10% raise, 'Marketing' gets a 15% raise, and all other departments get a 5% raise. 3. Create a new column reflecting this raise. Which of the following Snowpark code snippets achieves these transformations correctly and efficiently? (Select all that apply)

                          Answer: A,B

                          Explanation:
                          Options A and C are correct. Option A uses 'concat for string concatenation and 'when' for conditional salary calculation, which is a standard and efficient approach in Snowpark. Option C uses a Snowflake expression to achieve the same conditional salary calculation, which can be more concise for complex conditions and may leverage Snowflake's optimization. Option B is incorrect, because the '+' string concatenation will not work; string concatenation in Snowpark should happen using the 'concat' function. Option D is less efficient because it uses a Python UDF, which involves serialization/deserialization overhead. Option E will not work because of syntax 'select(' 'Y does not exist, therefore the user will need to specify all the columns. Also, there is a column called ' ' which will break the processing.


                          NEW QUESTION # 27
                          You are working with a data science team that needs to create Snowpark DataFrames from various file types (CSV, JSON, Parquet, and XML) stored in different locations (internal stages, external stages on AWS S3, and Azure Blob Storage). The team wants a unified and reusable function to create DataFrames, abstracting away the specific file format and location details. Which of the following approaches using Snowpark Python API will provide the MOST flexible and maintainable solution?

                          Answer: C

                          Explanation:
                          Option C provides the best balance of flexibility, maintainability, and conciseness. Using 'getattr(session.read, file_format)' allows dynamically calling the appropriate 'session.read' method (e.g., 'session.read.csv', 'session.read.json') based on a string parameter. Passing additional configuration through a dictionary allows customizing the read operation without modifying the core function. Options A, B, D, and E are less flexible, more verbose, or less efficient.


                          NEW QUESTION # 28
                          A data scientist is developing a Snowpark application that needs to authenticate to Snowflake using Key Pair Authentication. Which of the following steps are essential for configuring the Snowflake CLI to enable Key Pair Authentication and then correctly create a Snowpark session? (Select TWO)

                          Answer: C,D

                          Explanation:
                          Key Pair Authentication requires generating an RSA key pair (Option A) and configuring the Snowflake user with the public key (Option D). Setting the 'AUTHENTICATOR parameter to 'snowflake' is the correct approach. 'EXTERNALBROWSER uses the web-based browser authentication, while the private key is specified within the connection parameters directly, not the Snowflake CLI config.


                          NEW QUESTION # 29
                          ......

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