Snowflake DSA-C03 : SnowPro Advanced: Data Scientist Certification Exam

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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are analyzing sales data in Snowflake using Snowpark to identify seasonality. You have a table named 'SALES DATA with columns 'SALE DATE (TIMESTAMP NTZ) and 'AMOUNT (NUMBER). You want to calculate the rolling average sales for each week over a period of 12 weeks using a Snowpark DataFrame. Which of the following Snowpark code snippets correctly implements this calculation?

A)

B)

C)

D)

E)

2. You are working with a large dataset in Snowflake and need to build a machine learning model using scikit-learn in Python. You want to leverage Snowflake's compute resources for feature engineering to speed up the process. Which of the following approaches correctly combines Snowflake's SQL capabilities with scikit-learn for feature engineering and model training, while minimizing data transfer between Snowflake and the Python environment?

A) Use the Snowflake Python Connector to execute individual SQL queries for each feature engineering step. Load the resulting features step-by-step into a Pandas DataFrame and train the scikit-learn model.
B) Use Snowflake external functions to invoke a remote service (e.g., AWS Lambda) for feature engineering. Pass data from Snowflake to the remote service, receive the engineered features back, and load them into a Pandas DataFrame for model training.
C) Create Snowflake User-Defined Functions (UDFs) in Python for complex feature engineering calculations. Call these UDFs within a SQL query to apply the feature engineering to the Snowflake data. Load the resulting features into a Pandas DataFrame and train the scikit-learn model.
D) Write a complex SQL query in Snowmake to perform all feature engineering, then load the resulting features into a Pandas DataFrame and train the scikit-learn model.
E) Implement the feature engineering steps directly in Python using Pandas and scikit-learn, then load the raw data into a Pandas DataFrame and apply the transformations. Finally, train the scikit-learn model.

3. A retail company is using Snowflake to store sales data'. They have a table called 'SALES DATA' with columns: 'SALE ID', 'PRODUCT D', 'SALE DATE', 'QUANTITY' , and 'PRICE'. The data scientist wants to analyze the trend of daily sales over the last year and visualize this trend in Snowsight to present to the business team. Which of the following approaches, using Snowsight and SQL, would be the most efficient and appropriate for visualizing the daily sales trend?

A) Write a SQL query that uses 'DATE TRUNC('day', SALE DATE)' to group sales by day and calculate the total sales (SUM(QUANTITY PRICE)). Use Snowsight's line chart option with the truncated date on the x-axis and total sales on the y-axis, filtering by 'SALE_DATE' within the last year. Furthermore, use moving average with window function to smooth the data.
B) Export all the data from the 'SALES DATA' table to a CSV file and use an external tool like Python's Matplotlib or Tableau to create the visualization.
C) Write a SQL query that calculates the daily total sales amount CSUM(QUANTITY PRICEY) for the last year and use Snowsight's charting options to generate a line chart with 'SALE DATE on the x-axis and daily sales amount on the y-axis.
D) Use the Snowsight web UI to manually filter the 'SALES_DATX table by 'SALE_DATE for the last year and create a bar chart showing 'SALE_ID count per day.
E) Create a Snowflake view that aggregates the daily sales data, then use Snowsight to visualize the view data as a table without any chart.

4. 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) 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.
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) 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.
D) Periodically sample a small subset of the recent transaction data and manually compare it with the training data using descriptive statistics (mean, standard deviation).
E) 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.

5. You are tasked with predicting the sales price of houses based on their size (square footage) using linear regression in Snowflake. You have a table named 'HOUSE PRICES' with columns 'SQUARE FOOTAGE' and 'SALES PRICE'. You want to calculate the slope and intercept using Snowflake SQL. Which of the following queries, considering potential NULL values in the data, is the MOST robust and statistically sound for calculating the slope and intercept for a simple linear regression model?

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

Solutions: