最新なDatabricks Databricks-Certified-Professional-Data-Scientist問題集（140題）、真実試験の問題を全部にカバー！

質問 1：
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 doesn't impact the maximizing value
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 is always very close to 1
正解：A
解説: (Pass4Test メンバーにのみ表示されます)

質問 2：
Which activity is performed in the Operationalize phase of the Data Analytics Lifecycle?
A. Transform existing variables
B. Try different variables
C. Try different analytical techniques
D. Define the process to maintain the model
正解：D
解説: (Pass4Test メンバーにのみ表示されます)

質問 3：
You are building a classifier off of a very high-dimensiona data set similar to shown in the image with 5000 variables (lots of columns, not that many rows). It can handle both dense and sparse input. Which technique is most suitable, and why?

A. Random forest because it is an ensemble method
B. Naive Bayes, because Bayesian methods act as regularlizers
C. k-nearest neighbors, because it uses local neighborhoods to classify examples
D. Logistic regression with L1 regularization, to prevent overfitting
正解：D
解説: (Pass4Test メンバーにのみ表示されます)

質問 4：
In which lifecycle stage are test and training data sets created?
A. Data preparation
B. Model building
C. Discovery
D. Model planning
正解：B
解説: (Pass4Test メンバーにのみ表示されます)

質問 5：
Which of the following technique can be used to the design of recommender systems?
A. 1 and 3
B. Collaborative filtering
C. Power iteration
D. Naive Bayes classifier
E. 2 and 3
正解：B
解説: (Pass4Test メンバーにのみ表示されます)

質問 6：
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. 0
B. 1
C. n/2
D. 2
正解：B
解説: (Pass4Test メンバーにのみ表示されます)

質問 7：
Which of the following statement is true for the R square value in the regression model?
A. When R square =0, all the residual are equal to 1
B. R-squared never decreases upon adding more independent variables.
C. When R square =1 , all the residuals are equal to 0
D. R square can be increased by adding more variables to the model.
正解：B,C,D