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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. You're developing a model to predict customer churn using Snowflake. Your dataset is large and continuously growing. You need to implement partitioning strategies to optimize model training and inference performance. You consider the following partitioning strategies: 1. Partitioning by 'customer segment (e.g., 'High-Value', 'Medium-Value', 'Low-Value'). 2. Partitioning by 'signup_date' (e.g., monthly partitions). 3. Partitioning by 'region' (e.g., 'North America', 'Europe', 'Asia'). Which of the following statements accurately describe the potential benefits and drawbacks of these partitioning strategies within a Snowflake environment, specifically in the context of model training and inference?

A) Using clustering in Snowflake on top of partitioning will always improve query performance significantly and reduce compute costs irrespective of query patterns.
B) Implementing partitioning requires modifying existing data loading pipelines and may introduce additional overhead in data management. If the cost of partitioning outweighs the performance gains, it's better to rely on Snowflake's built-in micro-partitioning alone. Also, data skew in partition keys is a major concern.
C) Partitioning by 'signup_date' is ideal for capturing temporal dependencies in churn behavior and allows for easy retraining of models with the latest data. It also naturally aligns with a walk-forward validation approach. However, it might not be effective if churn drivers are independent of signup date.
D) Partitioning by 'region' is useful if churn is heavily influenced by geographic factors (e.g., local market conditions). It can improve query performance during both training and inference when filtering by region. However, it can create data silos, making it difficult to build a global churn model that considers interactions across regions. Furthermore, the 'region' column must have low cardinality.
E) Partitioning by 'customer_segment' is beneficial if churn patterns are significantly different across segments, allowing for training separate models for each segment. However, if any segment has very few churned customers, it may lead to overfitting or unreliable models for that segment.

2. You're developing a model to predict equipment failure using sensor data stored in Snowflake. The dataset is highly imbalanced, with failure events (positive class) being rare compared to normal operation (negative class). To improve model performance, you're considering both up-sampling the minority class and down-sampling the majority class. Which of the following statements regarding the potential benefits and drawbacks of combining up-sampling and down-sampling techniques in this scenario are TRUE? (Select TWO)

A) Combining up-sampling and down-sampling can lead to a more balanced dataset, potentially improving the model's ability to learn patterns from both classes without introducing excessive bias from solely up-sampling.
B) Over-sampling, combined with downsampling, makes the model more prone to overfitting since this causes the model to train on a large dataset.
C) The optimal sampling ratio for both up-sampling and down-sampling must always be 1:1, regardless of the initial class distribution.
D) Down-sampling, when combined with up-sampling, can exacerbate the risk of losing important information from the majority class, leading to underfitting, especially if the majority class is already relatively small.
E) Using both up-sampling and down-sampling always guarantees improved model performance compared to using only one of these techniques, regardless of the dataset characteristics.

3. You are building a binary classification model in Snowflake to predict customer churn based on historical customer data, including demographics, purchase history, and engagement metrics. You are using the SNOWFLAKE.ML.ANOMALY package. You notice a significant class imbalance, with churn representing only 5% of your dataset. Which of the following techniques is LEAST appropriate to handle this class imbalance effectively within the SNOWFLAKE.ML framework for structured data and to improve the model's performance on the minority (churn) class?

A) Downsampling the majority class to create a more balanced training dataset within Snowflake using SQL before feeding the data to the modeling function.
B) Applying a SMOTE (Synthetic Minority Over-sampling Technique) or similar oversampling technique to generate synthetic samples of the minority class before training the model outside of Snowflake, and then loading the augmented data into Snowflake for model training.
C) Adjusting the decision threshold of the trained model to optimize for a specific metric, such as precision or recall, using a validation set. This can be done by examining the probability outputs and choosing a threshold that maximizes the desired balance.
D) Using the 'sample_weight' parameter in the 'SNOWFLAKE.ML.ANOMALY.FIT function to assign higher weights to the minority class instances during model training.
E) Using a clustering algorithm (e.g., K-Means) on the features and then training a separate binary classification model for each cluster to capture potentially different patterns of churn within different customer segments.

4. You are deploying a large language model (LLM) to Snowflake using a user-defined function (UDF). The LLM's model file, '11m model.pt', is quite large (5GB). You've staged the file to Which of the following strategies should you employ to ensure successful deployment and efficient inference within Snowflake? Select all that apply.

A) Increase the warehouse size to XLARGE or larger to provide sufficient memory for loading the large model into the UDF environment.
B) Leverage Snowflake's Snowpark Container Services to deploy the LLM as a separate containerized application and expose it via a Snowpark API. Then call that endpoint from snowflake.
C) Use the 'IMPORTS' clause in the UDF definition to reference Ensure the UDF code loads the model lazily (i.e., only when it's first needed) to minimize startup time and memory usage.
D) Use the 'PUT' command with to compress the model file before staging it. Snowflake will automatically decompress it during UDF execution.
E) Split the large model file into smaller chunks and stage each chunk separately. Reassemble the model within the UDF code before inference.

5. You are building a machine learning pipeline in Snowflake using Snowpark Python. You have completed the data preparation and feature engineering steps and now need to train a model. You want to track the performance of different model versions and hyperparameters using MLflow. You are considering these deployment strategies. Which of the deployment strategies allows automatic logging of metrics, parameters, and model artifacts to MLflow for each training run without requiring explicit MLflow logging code?

A) Train the model within a Snowpark Python UDF. Use a Snowflake stage to store MLflow artifacts.
B) Train the model within a Snowpark Python stored procedure. Use a Snowflake stage to store MLflow artifacts.
C) Use the Snowpark MLAPI and its integration with MLflow's autologging feature. Enable autologging before starting the training run. Deploy the model to Snowflake as a UDF.
D) Train the model using Snowpark's DataFrame API directly in a Snowflake worksheet. Manually create a log file with metrics and model parameters and upload it to a Snowflake stage.
E) Train the model locally on your development machine and manually log metrics and artifacts to MLflow using the MLflow API. Then, deploy the trained model to Snowflake as a UDF or stored procedure.

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