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Databricks-Certified-Professional-Data-Scientist Exam Questions
- Topic 1: A intermediate understanding of the steps in the machine learning lifecycle/ Model training, selection, and production A complete understanding of the basics of machine learning/ in-sample vs. out-of sample data
- Topic 2: Applied statistics concepts/ bias-variance tradeoff
- Topic 3: A complete understanding of basic machine learning algorithms and techniques/ Unsupervised techniniques like K-means and PCA
- Topic 4: A complete understanding of the basics of machine learning model management/ Linear, logistic, and regularized regression
- Topic 5: Tree-based models like decision trees, random forest and gradient boosted trees/ Categories of machine learning
- Topic 6: Specific algorithms like ALS for recommendation and isolation forests for outlier detection/ Logging and model organization with MLflow
Free Databricks Databricks-Certified-Professional-Data-Scientist Exam Actual Questions
Note: Premium Questions for Databricks-Certified-Professional-Data-Scientist were last updated On Dec. 14, 2024 (see below)
You are working in a data analytics company as a data scientist, you have been given a set of various types of Pizzas available across various premium food centers in a country. This data is given as numeric values like Calorie. Size, and Sale per day etc. You need to group all the pizzas with the similar properties, which of the following technique you would be using for that?
You are having 1000 patients' data with the height and age. Where age in years and height in meters. You wanted to create cluster using this two attributes. You wanted to have near equal effect for both the age and height while creating the cluster. What you can do?
Refer to the exhibit.
You are building a decision tree. In this exhibit, four variables are listed with their respective values of info-gain.
Based on this information, on which attribute would you expect the next split to be in the decision tree?
What is the best way to evaluate the quality of the model found by an unsupervised algorithm like k-means clustering, given metrics for the cost of the clustering (how well it fits the data) and its stability (how similar the clusters are across multiple runs over the same data)?
In which lifecycle stage are appropriate analytical techniques determined?
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