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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

Question 1

You are using Snowflake Cortex to perform sentiment analysis on customer reviews stored in a table called 'CUSTOMER REVIEWS' The table has a column containing the text of each review. You want to create a user-defined function (UDF) to extract sentiment score between the range of -1 to 1 using the 'snowflake_cortex.sentiment' function in Snowflake Cortex. Which of the following UDF definitions would correctly implement this, allowing it to be called directly on the column?

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

Question 2

A marketing team at 'RetailSphere' wants to segment their customer base using unstructured textual data (customer reviews) stored in a Snowflake VARIANT column named 'REVIEW TEXT within the table 'CUSTOMER REVIEWS'. They aim to identify distinct customer segments based on sentiment and topics discussed in their reviews. They want to use a Supervised Learning approach for this task. Which of the following strategies best describes the appropriate approach within Snowflake, considering performance and scalability? Assume you have pre-trained sentiment and topic models deployed as Snowflake external functions.

A. Extract the column, apply sentiment analysis and topic modeling using Python within a Snowflake UDF, and then perform K-Means clustering directly on the resulting features within Snowflake. Define the labels after clustering based on the majority class of the topics and sentiments in each cluster.
B. Extract the 'REVIEW TEXT column, apply sentiment analysis and topic modeling using Java within a Snowflake UDF, and then perform hierarchical clustering directly on the resulting features within Snowflake. Manually label the clusters after visual inspection.
C. Create a Snowflake external function to call a pre-trained sentiment analysis and topic modeling model hosted on AWS SageMaker. Apply these functions to the ' REVIEW_TEXT column to generate sentiment scores and topic probabilities. Subsequently, use these features as input to a supervised classification model (e.g., XGBoost) also deployed as a Snowflake external function, training on a manually labeled subset of reviews.
D. Create a Snowflake external function to call a pre-trained sentiment analysis and topic modeling model hosted on Azure ML. Apply these functions to the REVIEW_TEXT column to generate sentiment scores and topic probabilities. Subsequently, use these features as input to an unsupervised clustering algorithm (e.g., DBSCAN) within Snowflake, relying solely on data density to define segments.
E. Extract the ' REVIEW_TEXT column, manually categorize a small subset of reviews into predefined segments. Train a text classification model (e.g., using scikit-learn) externally, deploy it as a Snowflake external function, and then apply this function to the entire 'REVIEW TEXT column to predict segment assignments. Manually adjust cluster centroids to represent the manually labeled dataset.

Question 3

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. 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.
B. 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.
C. Check for null values in the input features passed to the UDF. A sudden increase in null values indicates a problem with data quality.
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. Monitor the average predicted probability score over time. A significant shift in the average score indicates data drift.

Question 4

You're a data scientist analyzing sensor data from industrial equipment stored in a Snowflake table named 'SENSOR READINGS' The table includes 'TIMESTAMP' , 'SENSOR ID', 'TEMPERATURE', 'PRESSURE', and 'VIBRATION'. You need to identify malfunctioning sensors based on outlier readings in 'TEMPERATURE' , 'PRESSURE' , and 'VIBRATION'. You want to create a dashboard to visualize these outliers and present a business case to invest in predictive maintenance. Select ALL of the actions that are essential for both effectively identifying sensor outliers within Snowflake and visualizing the data for a business presentation. (Multiple Correct Answers)

A. Implement a clustering algorithm (e.g., DBSCAN) within Snowflake using Snowpark Python to group similar sensor readings, identifying outliers as points that do not belong to any cluster or belong to very small clusters.
B. Directly connect the 'SENSOR_READINGS' table to a visualization tool and create a 3D scatter plot with 'TEMPERATURE, 'PRESSURE, and 'VIBRATION' on the axes, without any pre-processing or outlier detection in Snowflake.
C. Calculate basic statistical summaries (mean, standard deviation, min, max) for each sensor and each variable C TEMPERATURE, 'PRESSURE, and 'VIBRATION') and use that information to filter down to the most important sensor, prior to using the other techniques.
D. Calculate Z-scores for 'TEMPERATURE, 'PRESSURE, and 'VIBRATION' for each 'SENSOR_ID within a rolling window of the last 24 hours using Snowflake's window functions. Define outliers as readings with Z-scores exceeding a threshold (e.g., 3).
E. Create a Snowflake stored procedure to automatically flag outlier readings in a new column 'IS OUTLIER based on a predefined rule set (e.g., IQR method or Z-score threshold), and then use this column to filter data for visualization in a dashboard.

Question 5

You are tasked with building a model to predict customer churn. You have a table named in Snowflake with the following relevant columns: 'customer_id', 'login_date', , 'orders_placed', , and 'churned' (binary indicator). You want to engineer features that capture customer engagement over time using Snowpark for Python. Which of the following feature engineering steps, applied sequentially, are MOST effective in creating features indicative of churn risk?

A. 1. Calculate the maximum 'page_views' in a single day for each customer. 2. Calculate the total number of days with no 'login_date' for each customer. 3. Create a feature indicating if a customer has ever placed an order. 4. Use a simple boolean for the 'subscription_type' column.
B. 1. Calculate the average 'page_views' per week for each customer over the last 3 months using a window function. 2. Calculate the recency of the last order (days since last order) for each customer. 3. Create a feature indicating the change in average daily page views over the last month compared to the previous month. 4. Create a feature showing standard deviation of page_views per customer over the last 90 days.
C. 1. Calculate the total 'page_views' and 'orders_placed' for each customer without considering time. 2. Use one-hot encoding for the 'subscription_type' column.
D. 1. Calculate the number of days since the customer's last login, and use nulls instead of negative numbers to indicate inactivity. 2. Calculate the rolling 7-day average of 'orders_placed' using a window function, partitioning by 'customer_id' and ordering by 'login_date'. 3. Calculate the slope of a linear regression of page_views' over time for each customer, indicating the trend in engagement using Snowpark ML. 4. Calculate the percentage of weeks the customer logged in. 5. Create a feature showing standard deviation of page_views per customer over the last 90 days.
E. 1. Calculate the average 'page_views' per day for each customer. 2. Calculate the total number of for each customer. 3. Create a feature indicating whether the customer has a premium subscription ('subscription_type' = 'premium').

Solutions: