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

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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are training a binary classification model in Snowflake to predict customer churn using Snowpark Python. The dataset is highly imbalanced, with only 5% of customers churning. You have tried using accuracy as the optimization metric, but the model performs poorly on the minority class. Which of the following optimization metrics would be most appropriate to prioritize for this scenario, considering the imbalanced nature of the data and the need to correctly identify churned customers, along with a justification for your choice?

A) F 1-Score - as it balances precision and recall, providing a good measure for imbalanced datasets.
B) Accuracy - as it measures the overall correctness of the model.
C) Area Under the Receiver Operating Characteristic Curve (AUC-ROC) - as it measures the ability of the model to distinguish between the two classes, irrespective of the class distribution.
D) Log Loss (Binary Cross-Entropy) - as it penalizes incorrect predictions proportionally to the confidence of the prediction, suitable for probabilistic outputs.
E) Root Mean Squared Error (RMSE) - as it is commonly used for regression problems, not classification.

2. You've trained a model using Snowflake ML and want to deploy it for real-time predictions using a Snowflake UDF. To ensure minimal latency, you need to optimize the UDF's performance. Which of the following strategies and considerations are most important when creating and deploying a UDF for model inference in Snowflake to minimize latency, especially when the model is large (e.g., > 100MB)?
Select all that apply.

A) Use smaller warehouse size for UDF evaluation in order to reduce latency and compute costs.
B) Store the trained model as a BLOB within the UDF code itself to avoid external dependencies.
C) Use a Snowflake Stage to store the model file and load the model within the UDF using 'snowflake.snowpark.files.SnowflakeFile' to minimize memory footprint.
D) Utilize a Snowflake external function instead of a UDF if the model requires access to resources outside of Snowflake's environment.
E) Ensure the UDF code is written in Python and utilizes vectorized operations with libraries like NumPy to process data in batches efficiently.

3. You are building a data science pipeline in Snowflake to predict customer churn. The pipeline involves extracting data, transforming it using Dynamic Tables, training a model using Snowpark ML, and deploying the model for inference. The raw data arrives in a Snowflake stage daily as Parquet files. You want to optimize the pipeline for cost and performance. Which of the following strategies are MOST effective, considering resource utilization and potential data staleness?

A) Use a combination of Dynamic Tables for feature engineering and Snowpark ML for model training and deployment, ensuring proper dependency management and refresh intervals for each Dynamic Table based on data freshness requirements.
B) Load all data into traditional Snowflake tables and use scheduled tasks with stored procedures written in Python to perform the transformations and model training.
C) Schedule all data transformations and model training as a single large Snowpark Python script executed by a Snowflake task, ignoring data freshness requirements.
D) Implement a series of smaller Dynamic Tables, each responsible for a specific transformation step, with well-defined refresh intervals tailored to the data's volatility and the downstream model's requirements.
E) Use a single, large Dynamic Table to perform all transformations in one step, relying on Snowflake's optimization to handle dependencies and incremental updates.

4. You are using the NetworkX library in Snowpark Python to analyze social network data stored in a Snowflake table named 'USER CONNECTIONS', which has columns 'USER ID' and 'CONNECTED USER representing connections between users. You want to find the users with the highest 'betweenness centrality' to identify influential nodes in the network. Which Snowpark Python code snippet would correctly calculate and display the top 5 users with the highest betweenness centrality?

A)

B)

C)

D)

E)

5. A marketing team uses Snowflake to store customer purchase data'. They want to segment customers based on their spending habits using a derived feature called The 'PURCHASES' table has columns 'customer id' (IN T), 'purchase_date' (DATE), and 'purchase_amount' (NUMBER). The team needs a way to handle situations where a customer might have missing months (no purchases in a particular month). They want to impute a 0 spend for those months before calculating the average. Which approach provides the most accurate and robust calculation, especially when considering users with sparse purchase history?

A) Use a window function to calculate the average spend over a fixed window of the last 3 months, ignoring missing months in the calculation.
B) Calculate the total spend for each customer and divide by the number of months since their first purchase: / DATEDlFF(month, CURRENT DATE()) GROUP BY customer_id'.
C) Create a view containing all months for each customer, left join with the 'PURCHASES' table, impute 0 for null 'purchase_amounts values, and then calculate the average spend. Requires creating a helper table for all the month.
D) Calculate the average monthly spend directly from the 'PURCHASES' table without accounting for missing months: 'AVG(purchase_amount) GROUP BY customer_id, date_trunc('month',
E) Calculate the average spend only for customers with purchases in every month of the year. Ignore other customers in the analysis.

Solutions: