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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

Question 1

You are a data scientist working with a Snowflake table named 'CUSTOMER DATA' that contains a 'PHONE NUMBER' column stored as VARCHAR. The 'PHONE NUMBER' column sometimes contains non-numeric characters like hyphens and parentheses, and in some rows the data is missing. You need to create a new table 'CLEANED CUSTOMER DATA' with a column named 'CLEANED PHONE NUMBER that contains only the numeric part of the phone number (as VARCHAR) and replaces missing or invalid phone numbers with NULL. Which of the following Snowpark Python code snippets achieves this most efficiently, ensuring no errors occur during the data transformation, and considers Snowflake's performance best practices?

A. Option E
B. Option A
C. Option D
D. Option C
E. Option B

Question 2

You are preparing a dataset in Snowflake for a K-means clustering algorithm. The dataset includes features like 'age', 'income' (in USD), and 'number of_transactions'. 'Income' has significantly larger values than 'age' and 'number of_transactions'. To ensure that all features contribute equally to the distance calculations in K-means, which of the following scaling approaches should you consider, and why? Select all that apply:

A. Apply RobustScaler to handle outliers and then StandardScaler or MinMaxScaler to further scale the features.
B. Apply StandardScaler to all three features ('age', 'income', 'number_of_transactions') to center the data around zero and scale it to unit variance.
C. Apply PowerTransformer to transform income and StandardScaler to other features to handle skewness.
D. Do not scale the data, as K-means is robust to differences in feature scales.
E. Apply MinMaxScaler to all three features to scale them to a range between O and 1 .

Question 3

A telecom company, 'ConnectPlus', observes that the individual call durations of its customers are heavily skewed towards shorter calls, following an exponential distribution. A data science team aims to analyze call patterns and requires to perform hypothesis testing on the average call duration. Which of the following statements regarding the applicability of the Central Limit Theorem (CLT) in this scenario are correct if the sample size is sufficiently large?

A. The CLT is applicable as long as the sample size is reasonably large (typically n > 30), and the distribution of sample means will be approximately normal. The specific minimum sample size depends on the severity of the skewness.
B. The CLT is not applicable because the population distribution (call durations) is heavily skewed.
C. The CLT is applicable, and the sample mean will converge to the population median.
D. The CLT is applicable only if the sample size is extremely large (e.g., greater than 10,000), due to the exponential distribution's heavy tail.
E. The CLT is applicable, and the distribution of sample means of call durations will approximate a normal distribution, regardless of the skewness of the individual call durations.

Question 4

You are building a fraud detection model using Snowflake data'. One of the features is 'transaction_amount', which has a highly skewed distribution and contains outlier values. Which scaling technique is most appropriate to handle this situation effectively in Snowflake, considering the need to minimize the impact of outliers and preserve the shape of the distribution as much as possible, before feeding the data into a machine learning model? Assume you have sufficient compute resources.

A. Power Transformer (Yeo-Johnson or Box-Cox)
B. RobustScaler (using interquartile range)
C. StandardScaler (Z-score normalization)
D. MinMaxScaler (Min-Max scaling)
E. No scaling is needed as tree-based models are robust to skewed data.

Question 5

You are building a binary classification model in Snowflake to predict customer churn based on historical customer data, including demographics, purchase history, and engagement metrics. You are using the SNOWFLAKE.ML.ANOMALY package. You notice a significant class imbalance, with churn representing only 5% of your dataset. Which of the following techniques is LEAST appropriate to handle this class imbalance effectively within the SNOWFLAKE.ML framework for structured data and to improve the model's performance on the minority (churn) class?

A. Applying a SMOTE (Synthetic Minority Over-sampling Technique) or similar oversampling technique to generate synthetic samples of the minority class before training the model outside of Snowflake, and then loading the augmented data into Snowflake for model training.
B. Using the 'sample_weight' parameter in the 'SNOWFLAKE.ML.ANOMALY.FIT function to assign higher weights to the minority class instances during model training.
C. Downsampling the majority class to create a more balanced training dataset within Snowflake using SQL before feeding the data to the modeling function.
D. Using a clustering algorithm (e.g., K-Means) on the features and then training a separate binary classification model for each cluster to capture potentially different patterns of churn within different customer segments.
E. Adjusting the decision threshold of the trained model to optimize for a specific metric, such as precision or recall, using a validation set. This can be done by examining the probability outputs and choosing a threshold that maximizes the desired balance.

Solutions: