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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A data scientist is tasked with predicting customer churn for a telecommunications company using Snowflake. The dataset contains call detail records (CDRs), customer demographic information, and service usage data'. Initial analysis reveals a high degree of multicollinearity between several features, specifically 'total_day_minutes', 'total_eve_minutes', and 'total_night_minutes'. Additionally, the 'state' feature has a large number of distinct values. Which of the following feature engineering techniques would be MOST effective in addressing these issues to improve model performance, considering efficient execution within Snowflake?

A) Create interaction features by multiplying 'total_day_minutes' with 'customer_service_calls' and applying a target encoding to the 'state' feature.
B) Apply Principal Component Analysis (PCA) to reduce the dimensionality of the CDR features ('total_day_minutes', 'total_eve_minutes', 'total_night_minutes') and use one-hot encoding for the 'state' feature.
C) Calculate the Variance Inflation Factor (VIF) for each CDR feature and drop the feature with the highest VIE Apply frequency encoding to the 'state' feature.
D) Use a variance threshold to remove highly correlated CDR features and create a feature representing the geographical region (e.g., 'Northeast', 'Southwest') based on the 'state' feature using a custom UDF.
E) Apply min-max scaling to the CDR features to normalize them and use label encoding for the 'state' feature. Train a decision tree model, as it is robust to multicollinearity.

2. A data scientist is analyzing sales data in Snowflake to identify seasonal trends. The 'SALES TABLE' contains columns 'SALE DATE' (DATE) and 'SALE _ AMOUNT' (NUMBER). They want to calculate the average daily sales amount for each month and year in the dataset. Which of the following SQL queries will correctly achieve this, while also handling potential NULL values in 'SALE AMOUNT?

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

3. You have deployed a fraud detection model in Snowflake using Snowpark and are monitoring its performance. You observe a significant drift in the transaction data distribution compared to the data used during training. To address this, you want to implement a retraining strategy. Which of the following steps are MOST critical to automate the retraining process using Snowflake's features?

A) Develop a Python UDF that periodically calculates drift metrics (e.g., Population Stability Index) and triggers retraining when a threshold is exceeded. Use Snowflake's Task feature to schedule the UDF execution.
B) Build and deploy a new docker image for each retraining, containing the new model, and update the external function definition to point to the new image.
C) Configure Snowflake's data lineage features to automatically track the input data and model lineage for reproducibility.
D) Replace the existing model artifact in Snowflake's stage with the newly trained model using Snowpark's model registry functionality.
E) Create a Snowflake Stream on the transaction data table to capture changes since the last training run.

4. You are building a fraud detection model in Snowflake using Snowpark Python. You want to evaluate the model's performance, particularly focusing on identifying instances of fraud (minority class). Which combination of metrics provides the most comprehensive assessment for this imbalanced classification problem within the Snowflake environment, considering the need to minimize both false positives (legitimate transactions flagged as fraudulent) and false negatives (fraudulent transactions missed)?

A) Accuracy and ROC AUC.
B) Precision and Fl-score.
C) Accuracy and Recall.
D) ROC AUC and Recall.
E) Precision, Recall, and Fl-score.

5. You've built a customer churn prediction model in Snowflake, and are using the AUC as your primary performance metric. You notice that your model consistently performs well (AUC > 0.85) on your validation set but significantly worse (AUC < 0.7) in production. What are the possible reasons for this discrepancy? (Select all that apply)

A) The AUC metric is inherently unreliable and should not be used for model evaluation.
B) Your model is overfitting to the validation data. This causes to give high performance on validation set but less accurate in the real world.
C) The production environment has significantly more missing data compared to the training and validation environments.
D) Your training and validation sets are not representative of the real-world production data due to sampling bias.
E) There's a temporal bias: the customer behavior patterns have changed since the training data was collected.

Solutions: