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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You have successfully trained a binary classification model using Snowpark ML and deployed it as a UDF in Snowflake. The UDF takes several input features and returns the predicted probability of the positive class. You need to continuously monitor the model's performance in production to detect potential data drift or concept drift. Which of the following methods and metrics, when used together, would provide the MOST comprehensive and reliable assessment of model performance and drift in a production environment? (Select TWO)

A) Check for null values in the input features passed to the UDF. A sudden increase in null values indicates a problem with data quality.
B) Monitor the average predicted probability score over time. A significant shift in the average score indicates data drift.
C) Continuously calculate and track performance metrics like AUC, precision, recall, and Fl-score on a representative sample of labeled production data over regular intervals. Compare these metrics to the model's performance on the holdout set during training.
D) Monitor the volume of data processed by the UDF per day. A sudden drop in volume indicates a problem with the data pipeline.
E) Calculate the Kolmogorov-Smirnov (KS) statistic between the distribution of predicted probabilities in the training data and the production data over regular intervals. Track any substantial changes in the KS statistic.

2. You are tasked with building a Python stored procedure in Snowflake to train a Gradient Boosting Machine (GBM) model using XGBoost.
The procedure takes a sample of data from a large table, trains the model, and stores the model in a Snowflake stage. During testing, you notice that the procedure sometimes exceeds the memory limits imposed by Snowflake, causing it to fail. Which of the following techniques can you implement within the Python stored procedure to minimize memory consumption during model training?

A) Implement XGBoost's 'early stopping' functionality with a validation set to prevent overfitting. If the stored procedure exceeds the memory limits, the model cannot be saved. Always use larger virtual warehouse.
B) Use the 'hist' tree method in XGBoost, enable gradient-based sampling ('gosS), and carefully tune the 'max_depth' and parameters to reduce memory usage during tree construction. Convert all features to numerical if possible.
C) Reduce the sample size of the training data and increase the number of boosting rounds to compensate for the smaller sample. Use the 'predict_proba' method to avoid storing probabilities for all classes.
D) Convert the Pandas DataFrame used for training to a Dask DataFrame and utilize Dask's distributed processing capabilities to train the XGBoost model in parallel across multiple Snowflake virtual warehouses.
E) Write the training data to a temporary table in Snowflake, then use Snowflake's external functions to train the XGBoost model on a separate compute cluster outside of Snowflake. Then upload the model to snowflake stage.

3. 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 E
B) Option C
C) Option A
D) Option D
E) Option B

4. You've deployed a regression model in Snowflake to predict product sales. After a month, you observe that the RMSE on your validation dataset has increased significantly compared to the initial deployment. Analyzing the prediction errors, you notice a pattern: the model consistently underestimates sales for products with a recent surge in social media mentions. Which of the following actions would be MOST effective in addressing this issue and improving the model's RMSE?

A) Incorporate a feature representing the number of social media mentions for each product into the model and retrain.
B) Decrease the learning rate of the optimization algorithm during retraining to avoid overshooting the optimal weights.
C) Retrain the model using only the most recent data (e.g., last week) to adapt to the changing sales patterns.
D) Increase the regularization strength of the model to prevent overfitting to the original training data.
E) Implement a moving average smoothing technique on the target variable (sales) before retraining the model.

5. You are exploring a large dataset of website user behavior in Snowflake to identify patterns and potential features for a machine learning model predicting user engagement. You want to create a visualization showing the distribution of 'session_duration' for different 'user_segments'. The 'user_segmentS column contains categorical values like 'New', 'Returning', and 'Power User'. Which Snowflake SQL query and subsequent data visualization technique would be most effective for this task?

A) Query: 'SELECT session_duration FROM user_behavior WHERE user_segments = 'New';- (repeated for each user segment). Visualization: Overlayed histograms showing the distribution of session duration for each user segment on the same axes.
B) Query: 'SELECT user_segments, AVG(session_duration) FROM user_behavior GROUP BY Visualization: Bar chart showing average session duration for each user segment.
C) Query: 'SELECT user_segments, MEDIAN(session_duration) FROM user_behavior GROUP BY user_segments;' Visualization: Box plot showing the distribution (quartiles, median, outliers) of session duration for each user segment.
D) Query: ' SELECT COUNT( ) ,user_segments FROM user_behavior GROUP BY user_segments;' Visualization: Pie chart showing proportion of each segment.
E) Query: 'SELECT user_segments, APPROX 0.25), APPROX 0.5), APPROX_PERCENTlLE(session_duration, 0.75) FROM user_behavior GROUP BY user_segments;' Visualization: Scatter plot where each point represents a user segment and the x,y coordinates represent session duration at 25th and 75th percentiles respectively.

Solutions: