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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. 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) Configure Snowflake's data lineage features to automatically track the input data and model lineage for reproducibility.
B) 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.
C) Create a Snowflake Stream on the transaction data table to capture changes since the last training run.
D) 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.
E) Replace the existing model artifact in Snowflake's stage with the newly trained model using Snowpark's model registry functionality.

2. A data scientist is exploring customer purchase data in Snowflake to identify high-value customer segments. They have a table named 'CUSTOMER TRANSACTIONS with columns 'CUSTOMER ID', 'TRANSACTION_DATE', and 'PURCHASE_AMOUNT'. They want to calculate the interquartile range (IQR) of 'PURCHASE AMOUNT for each customer. Which SQL query using Snowsight is the most efficient and accurate way to calculate and display the IQR for each 'CUSTOMER ID?

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

3. A healthcare provider has a Snowflake table 'MEDICAL RECORDS containing patient notes stored as unstructured text in a column called 'NOTE TEXT. They want to identify different patient groups based on the topics discussed in these notes. They aim to use a combination of unsupervised and supervised learning. Which of the following represents a robust workflow to achieve this goal?

A) Use a Snowflake external function to call a pre-trained topic modeling model (e.g., BERTopic) hosted on Google Cloud A1 Platform. Assign topic probabilities to each patient note. Then, perform K-Means clustering on the topic probabilities to identify patient segments. No manual labeling is performed.
B) Perform topic modeling (e.g., LDA) directly on the 'NOTE_TEXT column using a Python UDF in Snowflake. Manually label a subset of the resulting topics. Then, train a supervised classifier (e.g., Naive Bayes) to predict the identified topics for new patient notes.
C) MultiOutputClassifier wrapped around a Logistic Regression model) within Snowflake (using Snowpark), using the original 'NOTE TEXT as input features (TF-IDF or word embeddings) and the manually assigned topic labels as target variables. Use the trained model to classify the remaining patient notes into relevant patient groups.
D) Perform topic modeling on a sample of the 'NOTE TEXT data using a Snowflake Python UDF. Manually review the top documents for each identified topic, and assign labels describing the patient group represented by each topic. Train a supervised multi-label classification model (e.g., using scikit-learn's
E) Export all 'NOTE TEXT data to an extemal system, use an existing NLP pipeline for topic modeling and manual labeling, then create a Snowflake UDF that replicates this entire pipeline internally.

4. 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) 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.
D) Downsampling the majority class to create a more balanced training dataset within Snowflake using SQL before feeding the data to the modeling function.
E) 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.

5. You have deployed a fraud detection model in Snowflake that predicts the probability of a transaction being fraudulent. After a month, you observe that the model's precision has significantly dropped. You suspect data drift. Which of the following actions would be MOST effective in identifying and quantifying the data drift in Snowflake, assuming you have access to the transaction data before and after deployment?

A) Use Snowflake's built-in profiling capabilities to generate summary statistics for the training data. Compare these summary statistics with the statistics generated for recent transaction data. If significant differences are observed, assume data drift.
B) Calculate the Jensen-Shannon Divergence between the probability distributions of predicted fraud scores on the training set and the current production data set.
C) Periodically sample a small subset of the recent transaction data and manually compare it with the training data using descriptive statistics (mean, standard deviation).
D) Retrain the model daily with the most recent transaction data without performing any explicit data drift analysis, relying on the model to adapt to the changes.
E) Create a UDF in Snowflake to calculate the Kolmogorov-Smirnov (KS) statistic for each feature between the training data and the recent transaction data. Then, create an alert if the KS statistic exceeds a predefined threshold for any feature.

Solutions: