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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 train a machine learning model using scikit-learn. The training data resides in a Snowflake table named 'SALES DATA. You need to pass the feature columns (e.g., 'PRICE, 'QUANTITY) and the target column ('REVENUE) dynamically to the stored procedure. Which of the following approaches is the MOST secure and efficient way to achieve this, preventing SQL injection vulnerabilities and ensuring data integrity within the stored procedure?

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

2. A data scientist is building a model in Snowflake to predict customer churn. They have a dataset with features like 'age', 'monthly_spend', 'contract_length', and 'complaints'. The target variable is 'churned' (0 or 1). They decide to use a Logistic Regression model. However, initial performance is poor. Which of the following actions could MOST effectively improve the model's performance, considering best practices for Supervised Learning in a Snowflake environment focused on scalable and robust deployment?

A) Reduce the number of features by randomly removing some columns, as this always prevents overfitting.
B) Ignore missing values in the dataset as the Logistic Regression model will handle it automatically without skewing the results.
C) Increase the learning rate significantly to speed up convergence during training.
D) Fit a deep neural network with numerous layers directly within Snowflake without any data preparation, as this will automatically extract complex patterns.
E) Implement feature scaling (e.g., StandardScaler or MinMaxScaler) on numerical features within Snowflake, before training the model. Leverage Snowflake's user-defined functions (UDFs) for transformation and then train the model.

3. You are tasked with training a model within Snowflake to predict customer churn for a telecommunications company. The dataset is stored in a Snowflake table named 'CUSTOMER DATA. The features include 'age', and 'data_usage'. The target variable is 'churned' (boolean). You want to use the SNOWFLAKE.ML.ANACONDA INTEGRATION to leverage Scikit-learn for model training. Which of the following code snippets correctly performs model training with Snowflake ML, addressing potential issues like feature scaling and data type handling within the stored procedure?

A)

B)

C)

D)

4. You have built and deployed a model to predict the likelihood of loan default using Snowpark and deployed as a Snowflake UDF. You are using a separate Snowflake table 'LOAN APPLICATIONS' as input, which contains current applicant data'. After several weeks in production, you observe that the model's accuracy has significantly dropped. The original training data was collected during a period of low interest rates and stable economic conditions. Which of the following strategies are the MOST effective for identifying potential causes of this performance degradation and determining if a model retrain is necessary, in the context of Snowflake?

A) Monitor the model's precision and recall using a dedicated monitoring dashboard built on top of the model's predictions and actual loan outcomes (once available). Create a Snowflake alert that triggers when either metric falls below a predefined threshold.
B) Regularly sample data from the ' LOAN_APPLICATIONS table and manually compare it to the original training data. This provides a qualitative assessment of potential changes.
C) Compare the distribution of input features in the 'LOAN_APPLICATIONS table to the distribution of the features in the original training dataset using Snowflake's statistical functions (e.g., APPROX_COUNT DISTINCT, &AVG', 'STDDEV'). Significant deviations indicate data drift.
D) Assume the model is no longer valid due to changing economic conditions and immediately retrain the model with the latest available data without further investigation.
E) Re-run the original model training code with the 'LOAN_APPLICATIONS table as input and compare the resulting model coefficients to the coefficients of the deployed model. Significant differences indicate model decay.

5. 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) Serialize the scikit-learn model using 'joblib' instead of 'pickle' for potentially faster deserialization within the UDF.
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) Increase the warehouse size to improve the overall compute resources available for UDF execution.

Solutions: