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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are developing a Python stored procedure in Snowflake to predict sales for a retail company. You want to incorporate external data (e.g., weather forecasts) into your model. Which of the following methods are valid and efficient ways to access and use external data within your Snowflake Python stored procedure?

A) Load the external data into a Snowflake table and then query the table from within the Python stored procedure using the Snowflake Connector for Pythom
B) Use a Snowflake Pipe to continuously ingest external data from a cloud storage location and access the data within the stored procedure.
C) Embed the external data directly into the Python stored procedure's code as a dictionary or JSON object.
D) Directly call external APIs within the Python stored procedure using libraries like 'requests'. Snowflake's network policy must be configured to allow outbound connections.
E) Use a Snowflake external function to pre-process the external data and then pass the processed data as input parameters to the Python stored procedure.

2. You are tasked with optimizing the hyperparameter tuning process for a complex deep learning model within Snowflake using Snowpark Python. The model is trained on a large dataset stored in Snowflake, and you need to efficiently explore a wide range of hyperparameter values to achieve optimal performance. Which of the following approaches would provide the MOST scalable and performant solution for hyperparameter tuning in this scenario, considering the constraints and capabilities of Snowflake?

A) Leveraging a distributed hyperparameter tuning framework like Ray Tune or Dask-ML within Snowpark Python, utilizing Snowflake's compute resources for parallel training and evaluation of different hyperparameter configurations.
B) Implementing a custom hyperparameter optimization algorithm using Snowpark Python UDFs, manually distributing the hyperparameter search across multiple Snowflake virtual warehouses, and aggregating the results in a central table.
C) Using a simple 'for' loop to iterate through a predefined set of hyperparameter combinations, training the model for each combination within a Snowpark Python UDF, and storing the results in a Snowflake table.
D) Employing Scikit-learn's 'GridSearchCV' or 'RandomizedSearchCV' within a single Snowpark Python UDF, processing the entire dataset and hyperparameter search sequentially.
E) Using 'Hyperopt' within a Snowpark Python UDF, relying on its sequential optimization approach without any form of parallelization or distribution of the workload.

3. A data scientist is performing exploratory data analysis on a table named 'CUSTOMER TRANSACTIONS. They need to calculate the standard deviation of transaction amounts C TRANSACTION AMOUNT) for different customer segments CCUSTOMER SEGMENT). The 'CUSTOMER SEGMENT column can contain NULL values. Which of the following SQL statements will correctly compute the standard deviation, excluding NULL transaction amounts, and handling NULL customer segments by treating them as a separate segment called 'Unknown'? Consider using Snowflake-specific functions where appropriate.

A) Option C
B) Option D
C) Option E
D) Option A
E) Option B

4. You've built a complex machine learning model using scikit-learn and deployed it as a Python UDF in Snowflake. The UDF takes a JSON string as input, containing several numerical features, and returns a predicted probability However, you observe significant performance issues, particularly when processing large batches of data'. Which of the following approaches would be MOST effective in optimizing the performance of this UDF in Snowflake?

A) Rewrite the UDF in Java or Scala to leverage the JVM's performance advantages over Python in Snowflake.
B) Increase the warehouse size to improve the overall compute resources available for UDF execution.
C) Use Snowflake's vectorized UDF feature to process data in micro-batches, minimizing the overhead of repeated Python interpreter initialization.
D) Pre-process the input data outside of the UDF using SQL transformations, reducing the amount of data passed to the UDF and simplifying the Python code.
E) Serialize the scikit-learn model using 'joblib' instead of 'pickle' for potentially faster deserialization within the UDF.

5. You have built an external function to train a PyTorch model using SageMaker. The model training process requires a significant amount of CPU and memory. The training data is passed from Snowflake to the external function in batches. The external function code in AWS Lambda is as follows:

The Snowflake external function is defined as follows:

During testing, you encounter '500 Internal Server Error' from the external function consistently. Upon inspection of the Lambda logs, you find messages indicating 'PayloadTooLargeError'. What is the most likely cause and how do you mitigate it within the context of Snowflake and AWS Lambda?

A) The Lambda function is timing out before the model training can complete. Increase the Lambda function's timeout setting to allow sufficient time for the training process.
B) The IAM role associated with the Lambda function lacks the necessary permissions to invoke the SageMaker training job. Grant the Lambda function's IAM role the appropriate SageMaker permissions.
C) The size of the data being sent from Snowflake to the Lambda function exceeds the maximum payload size allowed by AWSAPI Gateway. Increase the maximum payload size limit in the API Gateway settings.
D) The Snowflake external function definition is incorrect. Change the 'RETURNS VARIANT clause to 'RETURNS VARCHAR as the Lambda function returns a JSON string.
E) The size of the data being sent from Snowflake to the Lambda function exceeds the maximum payload size allowed by AWS API Gateway. Implement data partitioning in Snowflake and send smaller batches of data to the Lambda function, aggregating the results in a separate table.

Solutions: