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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You are developing a churn prediction model and want to track its performance across different model versions using the Snowflake Model Registry. After registering a new model version, you need to log evaluation metrics (e.g., AUC, F 1-score) and custom tags associated with the training run. Assuming you have a registered model named 'churn_model' with version 'v2', which of the following code snippets demonstrates the correct way to log these metrics and tags using the Snowflake Python Connector and the 'ModelRegistry' API?

A)

B)

C)

D)

E)

2. You have successfully deployed a real-time prediction service using Snowpark Container Services, consuming events from a Kafka topic. The service leverages a large language model (LLM) stored in the Snowflake Model Registry. You observe that inference latency is high and the service is struggling to keep up with the incoming event rate. You need to optimize the service for higher throughput and lower latency. Which of the following actions, when implemented together, would most effectively improve the performance of your Snowpark Container Services deployment?

A) Enable autoscaling for the service based on CPU utilization. Remove all logging statements from the containerized application to reduce 1/0 overhead.
B) Increase the 'container.resources.memory' allocation for the service. Implement caching of frequently accessed data within the containerized application.
C) Switch to a smaller, less accurate LLM. Increase the 'container.resources.cpu' allocation for the service. Ensure data is pre-processed before sending to kafka.
D) Implement custom monitoring solution outside of snowflake and determine bottleneck of your application. Increase the container.resources.gpu allocation for the service.
E) Increase the number of replicas for the service. Implement batching within the containerized application to process multiple events in a single inference call.

3. You are building a model training pipeline in Snowflake using Snowpark Python. You want to leverage a pre-trained model from Hugging Face Transformers for a text classification task, fine-tuning it with your own labeled data stored in a Snowflake table named 'training_data'. You've chosen the 'transformers' library and plan to use a 'transformers.pipeline' for inference. Which of the following code snippets, when integrated into your Snowpark Python application, will correctly download the pre trained model and tokenizer, prepare the data, perform fine-tuning, and then save the fine-tuned model to a Snowflake stage?

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

4. A data science team is developing a churn prediction model using Snowpark Python. They have a feature engineering pipeline defined as a series of User Defined Functions (UDFs) that transform raw customer data stored in a Snowflake table named 'CUSTOMER DATA'. Due to the volume of data (billions of rows), they need to optimize UDF execution for performance. Which of the following strategies, when applied individually or in combination, will MOST effectively improve the performance of these UDFs within Snowpark?

A) Utilizing vectorized UDFs with NumPy data types wherever possible and carefully tuning batch sizes. Ensure that the input data is already sorted before passing to the UDF.
B) Using temporary tables to store intermediate results calculated by the UDFs instead of directly writing to the target table.
C) Converting Python UDFs to Java UDFs, compiling the Java code, and deploying as a JAR file in Snowflake. Using a larger warehouse size is always the best first option.
D) Repartitioning the DataFrame by a key that distributes data evenly across nodes before applying the UDFs, using the method and minimizing data shuffling.
E) Leveraging external functions that call an API endpoint hosted on a cloud provider to perform data transformation. The API endpoint should utilize a serverless architecture.

5. You are using Snowflake ML to train a binary classification model. After training, you need to evaluate the model's performance. Which of the following metrics are most appropriate to evaluate your trained model, and how do they differ in their interpretation, especially when dealing with imbalanced datasets?

A) Mean Squared Error (MSE): The average squared difference between the predicted and actual values. R-squared: Represents the proportion of variance in the dependent variable that is predictable from the independent variables. These are great for regression tasks.
B) Precision, Recall, F I-score, AUC-ROC, and Log Loss: Precision focuses on the accuracy of positive predictions; Recall focuses on the completeness of positive predictions; Fl-score balances Precision and Recall; AUC-ROC evaluates the separability of classes and Log Loss quantifies the accuracy of probabilities, especially valuable for imbalanced datasets because they provide a more nuanced view of performance than accuracy alone.
C) AUC-ROC: Measures the ability of the model to distinguish between classes. It is less sensitive to class imbalance than accuracy. Log Loss: Measures the performance of a classification model where the prediction input is a probability value between 0 and 1.
D) Confusion Matrix: A table that describes the performance of a classification model by showing the counts of true positive, true negative, false positive, and false negative predictions. This isnt a metric but representation of the metrics.
E) Accuracy: It measures the overall correctness of the model. Precision: It measures the proportion of positive identifications that were actually correct. Recall: It measures the proportion of actual positives that were identified correctly. Fl-score: It is the harmonic mean of precision and recall.

Solutions: