Databricks-Certified-Professional-Data-Scientist 試験問題を無料オンラインアクセス

Your customer provided you with 2. 000 unlabeled records three groups. What is the correct analytical method to use?

• A.Linear regression
• B.Semi Linear Regression
• C.Logistic regression
• D.K-means clustering
• E.Naive Bayesian classification

Suppose that we are interested in the factors that influence whether a political candidate wins an election. The outcome (response) variable is binary (0/1); win or lose. The predictor variables of interest are the amount of money spent on the campaign, the amount of time spent campaigning negatively and whether or not the candidate is an incumbent.
Above is an example of

• A.Linear Regression
• B.Logistic Regression
• C.Maximum likelihood estimation
• D.Recommendation system
• E.Hierarchical linear models

If you are trying to predict or forecast a discrete target value, then which is the correct options

• A.Density estimation algorithm
• B.Supervised Learning regression algorithms
• C.Supervised Learning classification algorithms
• D.Un supervised Learning

While working with Netflix the movie rating websites you have developed a recommender system that has produced ratings predictions for your data set that are consistently exactly 1 higher for the user-item pairs in your dataset than the ratings given in the dataset. There are n items in the dataset. What will be the calculated RMSE of your recommender system on the dataset?

• A.n/2
• B.1
• C.2
• D.0

In statistics, maximum-likelihood estimation (MLE) is a method of estimating the parameters of a statistical model. When applied to a data set and given a statistical model, maximum-likelihood estimation provides estimates for the model's parameters and the normalizing constant usually ignored in MLEs because

• A.The normalizing constant is always very close to 1
• B.The normalizing constant is often zero and can cause division by zero
• C.The normalizing constant only has a small impact on the maximum likelihood
• D.The normalizing constant doesn't impact the maximizing value