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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

Question 1

You are tasked with building a data science pipeline in Snowflake to predict customer churn. You have trained a scikit-learn model and want to deploy it using a Python UDTF for real-time predictions. The model expects a specific feature vector format. You've defined a UDTF named 'PREDICT CHURN' that loads the model and makes predictions. However, when you call the UDTF with data from a table, you encounter inconsistent prediction results across different rows, even when the input features seem identical. Which of the following are the most likely reasons for this behavior and how would you address them?

A. The scikit-learn model was not properly serialized and deserialized within the UDTF. Ensure the model is saved using 'joblib' or 'pickle' with appropriate settings for cross-platform compatibility and loaded correctly within the UDTF's 'process' method. Verify serialization/deserialization by testing it independently from Snowflake first.
B. The UDTF is not partitioning data correctly. Ensure the UDTF utilizes the 'PARTITION BY clause in your SQL query based on a relevant dimension (e.g., 'customer_id') to prevent state inconsistencies across partitions. This will isolate the impact of any statefulness within the function
C. The issue is related to the immutability of the Snowflake execution environment for UDTFs. To resolve this, cache the loaded model instance within the UDTF's constructor and reuse it for subsequent predictions. Using a global variable is also acceptable.
D. The input feature data types in the table do not match the expected data types by the scikit-learn model. Cast the input columns to the correct data types (e.g., FLOAT, INT) before passing them to the UDTF. Use explicit casting functions like 'TO DOUBLE and INTEGER in your SQL query.
E. There may be an error in model, where the 'predict method is producing different ouputs for the same inputs. Retraining the model will resolve the issue.

Question 2

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. 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.
B. Create a Snowflake view that aggregates the daily sales data, then use Snowsight to visualize the view data as a table without any chart.
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. 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.
E. 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.

Question 3

You are building a fraud detection model using Snowflake data'. One of the features is 'transaction_amount', which has a highly skewed distribution and contains outlier values. Which scaling technique is most appropriate to handle this situation effectively in Snowflake, considering the need to minimize the impact of outliers and preserve the shape of the distribution as much as possible, before feeding the data into a machine learning model? Assume you have sufficient compute resources.

A. MinMaxScaler (Min-Max scaling)
B. RobustScaler (using interquartile range)
C. No scaling is needed as tree-based models are robust to skewed data.
D. Power Transformer (Yeo-Johnson or Box-Cox)
E. StandardScaler (Z-score normalization)

Question 4

A retail company is using Snowflake to store transaction data'. They want to create a derived feature called 'customer _ recency' to represent the number of days since a customer's last purchase. The transactions table 'TRANSACTIONS has columns 'customer_id' (INT) and 'transaction_date' (DATE). Which of the following SQL queries is the MOST efficient and scalable way to derive this feature as a materialized view in Snowflake?

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

Question 5

You are deploying a machine learning model to Snowflake using a Python UDF. The model predicts customer churn based on a set of features. You need to handle missing values in the input data'. Which of the following methods is the MOST efficient and robust way to handle missing values within the UDF, assuming performance is critical and you don't want to modify the underlying data tables?

A. Pre-process the data in Snowflake using SQL queries to replace missing values with the mean for numerical features and the mode for categorical features before calling the UDF.
B. Use within the UDF to forward fill missing values. This assumes the data is ordered in a meaningful way, allowing for reasonable imputation.
C. Implement a custom imputation strategy using 'numpy.where' within the UDF, basing the imputation value on a weighted average of other features in the row.
D. Raise an exception within the UDF when a missing value is encountered, forcing the calling application to handle the missing values.
E. Use within the UDF, replacing missing values with a global constant (e.g., 0) defined outside the UDF. This constant is pre-calculated based on the training dataset's missing value distribution.

Solutions: