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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A Data Scientist is designing a machine learning model to predict customer churn for a telecommunications company. They have access to various data sources, including call logs, billing information, customer demographics, and support tickets, all residing in separate Snowflake tables. The data scientist aims to minimize bias and ensure data quality during the data collection phase. Which of the following strategies would be MOST effective for collecting and preparing the data for model training?

A) Perform exploratory data analysis (EDA) on each table to identify relevant features and potential biases. Use feature selection techniques to reduce dimensionality. Implement robust data validation checks to ensure data quality and consistency before joining the tables. Handle missing values strategically based on the specific column and its potential impact on the model.
B) Directly use all available columns from each table without any preprocessing to avoid introducing bias.
C) Use Snowflake's Data Marketplace to supplement the existing data with external datasets, regardless of their relevance to the churn prediction problem.
D) Randomly select a subset of data from each table to reduce computational complexity and speed up model training.
E) Create a single, wide table by performing a series of INNER JOINs on all tables using customer ID as the primary key. Handle missing values by imputing with the mean for numerical columns and 'Unknown' for categorical columns.

2. You have deployed a fraud detection model in Snowflake, predicting fraudulent transactions. Initial evaluations showed high accuracy. However, after a few months, the model's performance degrades significantly. You suspect data drift and concept drift. Which of the following actions should you take FIRST to identify and address the root cause?

A) Revert to a previous version of the model known to have performed well, while investigating the issue in the background.
B) Implement a SHAP (SHapley Additive exPlanations) analysis on recent transactions to understand feature importance shifts and potential concept drift.
C) Implement a data quality monitoring system to detect anomalies in input features, alongside calculating population stability index (PSI) to quantify data drift.
D) Increase the model's prediction threshold to reduce false positives, even if it means potentially missing more fraudulent transactions.
E) Immediately retrain the model with the latest available data, assuming data drift is the primary issue.

3. You are building a customer churn prediction model for a telecommunications company. You have a 'CUSTOMER DATA' table with a 'MONTHLY SPENDING' column that represents the customer's monthly bill amount. You want to binarize this column to create a feature indicating whether a customer is a 'High Spender' or 'Low Spender'. You decide that customers spending more than $75 are 'High Spenders'. Which of the following Snowflake SQL statements is the most efficient and correct way to achieve this, considering performance and readability, while avoiding potential NULL values in the resulting binarized column?

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

4. A data scientist is tasked with building a predictive maintenance model for industrial equipment. The data is collected from IoT sensors and stored in Snowflake. The raw sensor data is voluminous and contains noise, outliers, and missing values. Which of the following code snippets, executed within a Snowflake environment, demonstrates the MOST efficient and robust approach to cleaning and transforming this sensor data during the data collection phase, specifically addressing outlier removal and missing value imputation using robust statistics? Assume necessary libraries like numpy and pandas are available via Snowpark.

A)

B)

C)

D)

E)

5. You are building a machine learning model using Snowpark Python to predict house prices. The dataset contains a feature column named 'location' which contains free-form text descriptions of house locations. You want to leverage a pre-trained Large Language Model (LLM) hosted externally to extract structured location features like city, state, and zip code from the free-form text within Snowpark. You want to minimize the data transferred out of Snowflake. Which approach is most efficient and secure?

A) Use to load the 'location' column data into a Pandas DataFrame, call the external LLM API in your Python script to enrich the location data and then use to store the enriched data back into a Snowflake table.
B) Use the Snowflake Connector for Python to directly query the 'location' column and call the external LLM API from the connector. Then write the updated data into a new table.
C) Use Snowpark's 'createOrReplaceStage' to create an external stage pointing to the LLM API endpoint. Load the 'location' data into this stage and call the LLM API directly from the Snowflake stage using SQL.
D) Create a Snowflake External Function that calls the external LLM API. Pass the 'location' column data to the External Function and retrieve the structured location features. Then apply the External Function directly on the Snowpark DataFrame.
E) Create a Snowpark User-Defined Function (UDF) that calls the external LLM API. Pass the 'location' column data to the UDF and retrieve the structured location features. Then apply the UDF directly on the Snowpark DataFrame.

Solutions: