Snowflake SnowPro Advanced: Data Scientist Certification - DSA-C03 Exam Practice Test

You are tasked with preparing customer data for a churn prediction model in Snowflake. You have two tables: 'customers' (customer_id, name, signup_date, plan_id) and 'usage' (customer_id, usage_date, data_used_gb). You need to create a Snowpark DataFrame that calculates the total data usage for each customer in the last 30 days and joins it with customer information. However, the 'usage' table contains potentially erroneous entries with negative values, which should be treated as zero. Also, some customers might not have any usage data in the last 30 days, and these customers should be included in the final result with a total data usage of 0. Which of the following Snowpark Python code snippets will correctly achieve this?

You have trained a complex machine learning model using Snowpark for Python and are now preparing it for production deployment using Snowpark Container Services. You have containerized the model and pushed it to a Snowflake-managed registry. However, you need to ensure that only authorized users can access and deploy this model. Which of the following actions MUST you take to secure your model in the Snowflake Model Registry, ensuring appropriate access control, and minimizing the risk of unauthorized deployment or modification?

You are tasked with fine-tuning a Snowflake Cortex LLM model using your own labeled dataset to improve its performance on a specific sentiment analysis task related to customer reviews. You have already created a Snowflake stage 'my_stage' and uploaded your labeled data in CSV format to this stage. The labeled data contains two columns: 'review_text' and 'sentiment' (values: 'positive', 'negative', 'neutral'). Which of the following SQL commands, or sequences of commands, is MOST appropriate to initiate the fine-tuning process using the 'SNOWFLAKE.ML.FINETUNE LLM' function? Assume you have already set the necessary permissions for your role to access the model and stage.

You've trained a machine learning model using Scikit-learn and saved it as 'model.joblib'. You need to deploy this model to Snowflake. Which sequence of commands will correctly stage the model and create a Snowflake external function to use it for inference, assuming you already have a Snowflake stage named 'model_stage'?

You are building an image classification model within Snowflake to categorize satellite imagery based on land use types (residential, commercial, industrial, agricultural). The images are stored as binary data in a Snowflake table 'SATELLITE IMAGES. You plan to use a pre-trained convolutional neural network (CNN) from a library like TensorFlow via Snowpark Python UDFs. The model requires images to be resized and normalized before prediction. You have a Python UDF named that takes the image data and model as input and returns the predicted class. What steps are crucial to ensure optimal performance and scalability of the image classification process within Snowflake, considering the volume and velocity of incoming satellite imagery?

You are using a Snowflake Notebook to build a churn prediction model. You have engineered several features, and now you want to visualize the relationship between two key features: and , segmented by the target variable 'churned' (boolean). Your goal is to create an interactive scatter plot that allows you to explore the data points and identify any potential patterns.
Which of the following approaches is most appropriate and efficient for creating this visualization within a Snowflake Notebook?

You've developed a fraud detection model using Snowflake ML and want to estimate the expected payout (loss or gain) based on the model's predictions. The cost of investigating a potentially fraudulent transaction is $50. If a fraudulent transaction goes undetected, the average loss is $1000. The model's confusion matrix on a validation dataset is: Predicted Fraud Predicted Not Fraud Actual Fraud 150 50 Actual Not Fraud 20 780 Which of the following SQL queries in Snowflake, assuming you have a table 'FRAUD PREDICTIONS' with columns 'TRANSACTION ID', 'ACTUAL FRAUD', and 'PREDICTED FRAUD' (1 for Fraud, O for Not Fraud), provides the most accurate estimate of the expected payout for every 1000 transactions?

You are tasked with optimizing the hyperparameter tuning process for a complex deep learning model within Snowflake using Snowpark Python. The model is trained on a large dataset stored in Snowflake, and you need to efficiently explore a wide range of hyperparameter values to achieve optimal performance. Which of the following approaches would provide the MOST scalable and performant solution for hyperparameter tuning in this scenario, considering the constraints and capabilities of Snowflake?

You've developed a binary classification model using Snowpark ML to predict customer subscription renewal (0 for churn, 1 for renew). You want to visualize feature importance using a permutation importance technique calculated within Snowflake. You perform feature permutation and calculate the decrease in model performance (e.g., AUC) after each permutation. Suppose the following query represents the results of this process:

The 'feature_importance_results' table contains the following data:

Based on this output, which of the following statements are the MOST accurate interpretations regarding feature impact and model behavior?

You're working with a Snowflake stage named that contains several versions of your machine learning model, named 'model_vl .pkl' , 'model_v2.pkl' , and You want to programmatically list all files in the stage and retrieve the creation time of the latest version (i.e., using SnowSQL. Which of the following approaches is most efficient and correct?

You are working with a dataset of customer transaction logs stored in Snowflake. Due to legal restrictions, you are unable to directly access or analyze the entire dataset. However, you can query aggregate statistics. You need to estimate the standard error of the mean transaction amount using bootstrapping. Knowing that you cannot retrieve the individual transaction amounts directly, which of the following approaches, while technically feasible within Snowflake and its stored procedure capabilities, is the least appropriate and potentially misleading application of bootstrapping?

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.

You have deployed a vectorized Python UDF in Snowflake to perform sentiment analysis on customer reviews. The UDF uses a pre-trained transformer model loaded from a Stage. The model consumes a significant amount of memory (e.g., 5GB). Users are reporting intermittent 'Out of Memory' errors when calling the UDF, especially during peak usage. Which of the following strategies, used IN COMBINATION, would MOST effectively mitigate these errors and optimize resource utilization?

You are tasked with building a fraud detection model using Snowflake and Snowpark Python. The model needs to identify fraudulent transactions in real-time with high precision, even if it means missing some actual fraud cases. Which combination of optimization metric and model tuning strategy would be most appropriate for this scenario, considering the importance of minimizing false positives (incorrectly flagging legitimate transactions as fraudulent)?