Snowflake SnowPro Advanced: Data Scientist Certification : DSA-C03

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Snowflake DSA-C03 Exam Syllabus Topics:

Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

Question #1
A data scientist is tasked with building a real-time customer support system using Snowflake Cortex. The system needs to analyze incoming customer messages and categorize them into predefined issue types (e.g., billing, technical support, account management) for efficient routing to the appropriate support team. Considering the need for low latency and high accuracy, which of the following approaches would be the MOST suitable for implementing this categorization task using Snowflake Cortex, considering the costs and trade-offs involved?

A. Creating a series of SQL 'CASE' statements to categorize the messages based on keyword matching within the message text. Use regular expressions for more complex pattern matching.
B. Leveraging the Snowflake Cortex built-in categorization task-specific model (e.g., using the 'SNOWFLAKE.ML.PREDICT' function with the appropriate model name) to categorize incoming messages without any fine-tuning.
C. Directly calling the Snowflake Cortex 'COMPLETE' endpoint with a detailed prompt for each incoming message, instructing it to categorize the message based on the predefined issue types.
D. Fine-tuning a pre-trained language model within Snowflake using the 'CREATE SNOWFLAKE.ML.ANACONDA_MODEL' command on a dataset of historical customer messages and their corresponding issue types, then deploying this fine-tuned model for real-time categorization via a user-defined function (UDF).
E. Developing a custom Python UDF that uses a third-party LLM API (e.g., OpenAl) to categorize the messages and deploying it in Snowflake, handling API authentication and rate limiting within the UDF.

Question #2
You have a table in Snowflake named 'CUSTOMER DATA' with columns 'CUSTOMER D', 'PURCHASE AMOUNT', and 'RECENCY'. You want to perform feature scaling on 'PURCHASE AMOUNT' using Min-Max scaling and store the scaled values in a new column named 'SCALED PURCHASE _ AMOUNT'. Which of the following Snowflake SQL code snippets correctly implements this feature scaling? Note: Assume there are no NULL values in PURCHASE AMOUNT and you have privileges to create temporary tables and UDFs if necessary.

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

Question #3
A data scientist is using Snowflake to perform anomaly detection on sensor data from industrial equipment. The data includes timestamp, sensor ID, and sensor readings. Which of the following approaches, leveraging unsupervised learning and Snowflake features, would be the MOST efficient and scalable for detecting anomalies, assuming anomalies are rare events?

A. Use a Support Vector Machine (SVM) with a radial basis function (RBF) kernel trained on the entire dataset to classify data points as normal or anomalous. Implement the SVM model as a Snowflake UDF.
B. Calculate the moving average of sensor readings over a fixed time window using Snowflake SQL and flag data points that deviate significantly from the moving average as anomalies. No ML model needed.
C. Apply Autoencoders to the sensor data using a Snowflake external function. Data points are considered anomalous if the reconstruction error from the autoencoder exceeds a certain threshold.
D. Use K-Means clustering to group sensor readings into clusters and identify data points that are far from the cluster centroids as anomalies. No model training necessary.
E. Implement an Isolation Forest model. Train the Isolation Forest model on a representative sample of the sensor data and create UDF to score each row in snowflake.

Question #4
You are working with a large dataset of sensor readings stored in a Snowflake table. You need to perform several complex feature engineering steps, including calculating rolling statistics (e.g., moving average) over a time window for each sensor. You want to use Snowpark Pandas for this task. However, the dataset is too large to fit into the memory of a single Snowpark Pandas worker. How can you efficiently perform the rolling statistics calculation without exceeding memory limits? Select all options that apply.

A. Utilize the 'window' function in Snowpark SQL to define a window specification for each sensor and calculate the rolling statistics using SQL aggregate functions within Snowflake. Leverage Snowpark to consume the results of the SQL transformation.
B. Explore using Snowpark's Pandas user-defined functions (UDFs) with vectorization to apply custom rolling statistics logic directly within Snowflake. UDFs allow you to use Pandas within Snowflake without needing to bring the entire dataset client-side.
C. Increase the memory allocation for the Snowpark Pandas worker nodes to accommodate the entire dataset.
D. Break the Snowpark DataFrame into smaller chunks using 'sample' and 'unionAll', process each chunk with Snowpark Pandas, and then combine the results.
E. Use the 'grouped' method in Snowpark DataFrame to group the data by sensor ID, then download each group as a Pandas DataFrame to the client and perform the rolling statistics calculation locally. Then upload back to Snowflake.

Question #5
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. Schedule all data transformations and model training as a single large Snowpark Python script executed by a Snowflake task, ignoring 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. 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.
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.

Solutions: