Snowflake SnowPro Advanced: Data Scientist Certification - DSA-C03 FREE EXAM DUMPS QUESTIONS & ANSWERS

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?

You've built a machine learning model in scikit-learn and want to deploy it to Snowflake for real-time inference. You have the following options for deploying the model. Select all that apply and are considered a best practice for cost and time optimization:

A retail company, 'GlobalMart,' wants to optimize its product placement strategy in its physical stores. They have transactional data stored in Snowflake, capturing which items are purchased together in the same transaction. They aim to use association rule mining to identify frequently co-occurring items. Given the following simplified transactional data in a Snowflake table named 'SALES TRANSACTIONS:

Which of the following SQL-based approaches, combined with Snowpark Python for association rule generation (using a library like 'mlxtend'), would be the MOST efficient and scalable way to prepare this data for association rule mining, specifically focusing on converting it into a transaction-item matrix suitable for algorithms like Apriori? Assume 'spark' is a 'snowpark.Session' object connected to your Snowflake environment.

You are tasked with building a data science pipeline in Snowflake to predict customer churn. You have trained a scikit-learn model and want to deploy it using a Python UDTF for real-time predictions. The model expects a specific feature vector format. You've defined a UDTF named 'PREDICT CHURN' that loads the model and makes predictions. However, when you call the UDTF with data from a table, you encounter inconsistent prediction results across different rows, even when the input features seem identical. Which of the following are the most likely reasons for this behavior and how would you address them?

A data scientist is analyzing website conversion rates for an e-commerce platform. They want to estimate the true conversion rate with 95% confidence. They have collected data on 10,000 website visitors, and found that 500 of them made a purchase. Given this information, and assuming a normal approximation for the binomial distribution (appropriate due to the large sample size), which of the following Python code snippets using scipy correctly calculates the 95% confidence interval for the conversion rate? (Assume standard imports like 'import scipy.stats as St' and 'import numpy as np').

You are using Snowflake ML to predict housing prices. You've created a Gradient Boosting Regressor model and want to understand how the 'location' feature (which is categorical, representing different neighborhoods) influences predictions. You generate a Partial Dependence Plot (PDP) for 'location'. The PDP shows significantly different predicted prices for each neighborhood. Which of the following actions would be MOST appropriate to further investigate and improve the model's interpretability and performance?

You are analyzing website traffic data stored in a Snowflake table named 'WEB EVENTS. This table contains a 'TIMESTAMP' column representing when the event occurred and a 'PAGE VIEWS column indicating the number of page views for that event. You need to identify the day with the highest number of page views and also the day with lowest number of page views along with average number of page views. How can you accomplish this using Snowflake SQL?

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?

A financial institution suspects fraudulent activity based on unusual transaction patterns. They want to use association rule mining to identify relationships between different transaction attributes (e.g., transaction amount, location, time of day, merchant category code) that are indicative of fraud. The data is stored in a Snowflake table called 'TRANSACTIONS'. Which of the following considerations are CRITICAL when applying association rule mining in this fraud detection scenario?