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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Transformations and Operations | 35% | - Advanced operations
|
| Topic 2: Snowpark API and Development | 30% | - Python API fundamentals
|
| Topic 3: Performance and Best Practices | 10% | - Optimization techniques
|
| Topic 4: Snowpark Concepts and Architecture | 25% | - Session management and connection
|
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NEW QUESTION # 319
You have a Snowpark Python stored procedure 'process_data' that takes a Snowpark DataFrame as input, performs several data transformations using functions defined in a separate Python module 'data utils.py', and returns a transformed DataFrame. The 'data utils.py' file is located in your local directory. You want to register this stored procedure so that it can be called from Snowflake. Which of the following code snippets demonstrate(s) the correct way to register the stored procedure, ensuring that the 'data utils.py' module is available within the Snowpark environment? (Select TWO)
Answer: B,C
Explanation:
Option C correctly specifies the 'data_utils.py' file in the 'imports' parameter of the gsproc' decorator, making it available within the Snowpark environment. It also includes re-importing the 'data_utilS package within the function. Option D specifies the file in the 'imports parameter and uses the '_import_' function to load and use the module, which is a valid approach. Option A would error at runtime because "data_utils' won't be found unless it is in the same file as the stored procedure definition or the user has installed the python packages. Option B contains incorrect usage of 'return_type', and and doesn't account for importing 'data_utils.pV. Option E has the same problem as A as the dependent file 'data_utils.py' has not been imported. The 'stage_location' parameter specifies the stage where the Python file should be uploaded. The two correct ways ensure that the data_utils module will be loaded and accessible in the Snowpark environment.
NEW QUESTION # 320
You are working with Snowpark and need to persist the results of a DataFrame 'df to a Snowflake stage named 'my_stage'. You want to achieve the following: 1. Write the data in JSON format. 2. Use snappy compression. 3. Handle potential write errors gracefully. 4. Overwrite any existing files with the same name. Which of the following approaches can achieve these requirements? (Select all that apply)
Answer: C,D,E
Explanation:
Option A directly utilizes the .json(V method for writing JSON files with snappy compression, specifying the 'overwrite' mode, and the 'try-except block adds error handling. Option C specifies the format to JSON using format('json') with snappy compression and specifying the 'overwrite' mode, and using a 'try-except' block for error handling. Option E deals with write errors by wrapping the entire process inside try-except block. Options D is not correct since creating a UDF just for this purpose introduces unnecessary overhead compared to the simple and direct approaches provided by A and C. Option B is incorrect, while configuring the stage with a default file format is useful, it won't apply in this specific write operation using DataFrameWriter. We need to explicitly define format and compression for the operation.
NEW QUESTION # 321
You are building a Snowpark application that uses a Python UDF to perform sentiment analysis on customer reviews. The UDF relies on a large pre-trained machine learning model loaded from a file. During execution, you encounter 'Out of Memory' errors within the UDF. Considering the constraints of the Snowpark execution environment and the need to optimize resource usage, which of the following steps is the MOST effective in addressing this issue and ensuring the application's stability and performance?
Answer: C,E
Explanation:
Lazy loading can reduce initial memory footprint of the UDF. Further, the first time when model is requested, it will be loaded in the UDF and cached for subsequent calls. This avoids reloading the same model again and again. Optimizing the model can reduce the memory footprint of the model to the point it no longer causes out of memory issues. Increasing warehouse size may help but won't address the underlying issue. Breaking down the reviews doesn't solve the memory issue within each batch. Stored procedures do not necessarily have more memory allocated than UDFs.
NEW QUESTION # 322
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: B,C,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 # 323
A data scientist has developed a Snowpark Python stored procedure named 'model_training'. This procedure utilizes a large machine learning model and requires significant compute resources. The data scientist wants to optimize the cost and performance of running this stored procedure. Which of the following strategies would be the MOST effective for achieving this goal?
Answer: E
Explanation:
Specifying a warehouse size and using auto-suspend and auto-resume provides a balance between performance and cost. Option A might increase costs due to idle time. Option B relies on Snowflake's default warehouse, which might not be optimal. Option D could increase overall execution time due to overhead. Option E may not be feasible or efficient if the logic is heavily dependent on Python libraries. Therefore, specifically assigning an appropriate warehouse size with auto-suspend/resume is the most effective approach.
NEW QUESTION # 324
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