Snowflake DSA-C03 Exam Questions : SnowPro Advanced: Data Scientist Certification Exam

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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are evaluating a binary classification model built in Snowflake for predicting customer churn. You have access to the model's predictions on a holdout dataset, and you want to use both the ROC curve and the confusion matrix to comprehensively assess its performance. Which of the following statements regarding the interpretation and use of ROC curves and confusion matrices are correct in this scenario?

A) The confusion matrix allows you to calculate precision, recall, F I-score, and accuracy, which are all useful for understanding the model's performance in terms of correctly and incorrectly classified instances.
B) While the ROC curve is independent of the class distribution, the metrics derived from the confusion matrix (e.g., precision, recall) can be significantly affected by imbalanced datasets.
C) The ROC curve visualizes the trade-off between true positive rate (sensitivity) and false negative rate (1 - specificity) at various threshold settings.
D) In Snowflake, you can generate ROC curves and confusion matrices directly using the 'SYSTEM$PREDICT function with appropriate parameters and visualizing the results using a tool like Snowsight or Tableau.
E) The area under the ROC curve (AUC) provides a single scalar value representing the overall discriminatory power of the model, with a higher AUC indicating better performance. An AUC of 0.5 indicates that the model performs no better than random chance.

2. You are performing exploratory data analysis on a dataset containing customer transaction data in Snowflake. The dataset has a column named 'transaction_amount' and a column named 'customer_segment'. You want to analyze the distribution of transaction amounts for each customer segment using Snowflake's statistical functions. Which of the following approaches would BEST achieve this, providing insights into the central tendency and spread of the data?

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

3. You are a data scientist working with a Snowflake table named 'CUSTOMER TRANSACTIONS' that contains sensitive PII data, including customer names and email addresses. You need to create a representative sample of 1% of the data for model development, ensuring that the sample is anonymized and protects customer privacy. The sample must be reproducible for future model iterations.
Which of the following steps are most appropriate using Snowpark for Python and SQL?

A) Use the 'SAMPLE clause in a SQL query to extract 1% of the rows, then apply SHA256 hashing to the 'customer_name' and 'email_addresS columns within Snowpark using a UDF. Seed the sampling for reproducibility.
B) Create a new table using 'CREATE TABLE AS SELECT statement combined with 'SAMPLE clause and SHA256 hashing functions in SQL to create the sample and anonymize data. Manually seed the random number generator in Python before executing the SQL statement via Snowpark.
C) Use the 'QUALIFY OVER (ORDER BY RANDOM()) (SELECT COUNT( ) 0.01 FROM CUSTOMER_TRANSACTIONS)' clause with SHA256 on sensitive columns directly within a CREATE TABLE AS statement to generate an anonymized sample. The function should return only 1 percentage of row.
D) Use Snowpark DataFrame's 'sample' function with a fraction of 0.01 and a fixed random seed. Before sampling, create a view that masks 'customer_name' and 'email_address' columns, and then sample from the view.
E) Employ stratified sampling based on a customer segment column, then anonymize data. Use the TABLESAMPLE BERNOULLI function in SQL with a 1 percent sample rate. Apply SHA256 hashing to the 'customer_name' and 'email_addresS columns using SQL functions.

4. A data science team is developing a churn prediction model using Snowpark Python. They have a feature engineering pipeline defined as a series of User Defined Functions (UDFs) that transform raw customer data stored in a Snowflake table named 'CUSTOMER DATA'. Due to the volume of data (billions of rows), they need to optimize UDF execution for performance. Which of the following strategies, when applied individually or in combination, will MOST effectively improve the performance of these UDFs within Snowpark?

A) Converting Python UDFs to Java UDFs, compiling the Java code, and deploying as a JAR file in Snowflake. Using a larger warehouse size is always the best first option.
B) Leveraging external functions that call an API endpoint hosted on a cloud provider to perform data transformation. The API endpoint should utilize a serverless architecture.
C) Utilizing vectorized UDFs with NumPy data types wherever possible and carefully tuning batch sizes. Ensure that the input data is already sorted before passing to the UDF.
D) Using temporary tables to store intermediate results calculated by the UDFs instead of directly writing to the target table.
E) Repartitioning the DataFrame by a key that distributes data evenly across nodes before applying the UDFs, using the method and minimizing data shuffling.

5. You are deploying a machine learning model to Snowflake using a Python UDF. The model predicts customer churn based on a set of features. You need to handle missing values in the input data'. Which of the following methods is the MOST efficient and robust way to handle missing values within the UDF, assuming performance is critical and you don't want to modify the underlying data tables?

A) Raise an exception within the UDF when a missing value is encountered, forcing the calling application to handle the missing values.
B) Pre-process the data in Snowflake using SQL queries to replace missing values with the mean for numerical features and the mode for categorical features before calling the UDF.
C) Use within the UDF to forward fill missing values. This assumes the data is ordered in a meaningful way, allowing for reasonable imputation.
D) Implement a custom imputation strategy using 'numpy.where' within the UDF, basing the imputation value on a weighted average of other features in the row.
E) Use within the UDF, replacing missing values with a global constant (e.g., 0) defined outside the UDF. This constant is pre-calculated based on the training dataset's missing value distribution.

Solutions: