[2022年最新] 完璧Databricks-Certified-Professional-Data-Scientist問題集問題と解答で一年無料最速更新
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質問 65
Which of the below best describe the Principal component analysis
- A. Regression
- B. Classification
- C. Clustering
- D. Dimensionality reduction
- E. Collaborative filtering
正解: D
質問 66
Question-13. Which of the following is not the Classification algorithm?
- A. Neural Network
- B. Hidden Markov Models
- C. None of the above
- D. Logistic Regression
- E. Support Vector Machine
正解: C
解説:
Explanation
Logistic regression
Logistic regression is a model used for prediction of the probability of occurrence of an event. It makes use of several predictor variables that may be either numerical or categories.
Support Vector Machines
As with naive Bayes, Support Vector Machines (or SVMs) can be used to solve the task of assigning objects to classes. But the way this task is solved is completely different to the setting in naive Bayes.
Neural Network
Neural Networks are a means for classifying multidimensional objects.
Hidden Markov Models
Hidden Markov Models are used in multiple areas of machine learning, such as speech recognition, handwritten letter recognition, or natural language processing.
質問 67
Assume some output variable "y" is a linear combination of some independent input variables "A" plus some independent noise "e". The way the independent variables are combined is defined by a parameter vector B y=AB+e where X is an m x n matrix. B is a vector of n unknowns, and b is a vector of m values. Assuming that m is not equal to n and the columns of X are linearly independent, which expression correctly solves for B?
- A. Option D
- B. Option B
- C. Option A
- D. Option C
正解: A
解説:
Explanation
This is the standard solution of the normal equations for linear regression. Because A is not square, you cannot simply take its inverse.
質問 68
Marie is getting married tomorrow, at an outdoor ceremony in the desert. In recent years, it has rained only 5 days each year. Unfortunately, the weatherman has predicted rain for tomorrow. When it actually rains, the weatherman correctly forecasts rain 90% of the time. When it doesn't rain, he incorrectly forecasts rain 10% of the time. Which of the following will you use to calculate the probability whether it will rain on the day of Marie's wedding?
- A. Random Decision Forests
- B. Logistic Regression
- C. All of the above
- D. Naive Bayes
正解: D
解説:
Explanation
The sample space is defined by two mutually-exclusive events - it rains or it does not rain. Additionally, a third event occurs when the weatherman predicts rain. You should consider Bayes' theorem when the following conditions exist.
* The sample space is partitioned into a set of mutually exclusive events {A1, A2,... :An}.
* Within the sample space, there exists an event B: for which P(B) > 0.
* The analytical goal is to compute a conditional probability of the form: P( Ak B).
質問 69
RMSE is a good measure of accuracy, but only to compare forecasting errors of different models for a______, as it is scale-dependent.
- A. Between Variables
- B. All of the above are correct
- C. Particular Variable
- D. Among all the variables
正解: C
解説:
Explanation : The RMSE serves to aggregate the magnitudes of the errors in predictions for various times into a single measure of predictive power. RMSE is a good measure of accuracy, but only to compare forecasting errors of different models for a particular variable and not between variables, as it is scale-dependent.
質問 70
Which of the following are point estimation methods?
- A. MAP
- B. MLE
- C. MMSE
正解: A,B,C
解説:
Explanation
Point estimators
* minimum-variance mean-unbiased estimator (MVUE), minimizes the risk (expected loss) of the squared-error loss-function.
* best linear unbiased estimator (BLUE)
* minimum mean squared error (MMSE)
* median-unbiased estimator, minimizes the risk of the absolute-error loss function
* maximum likelihood (ML)
* method of moments, generalized method of moments
質問 71
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?
- A. You will be adding height with the numeric value 100
- B. You will be converting each height value to centimeters
- C. You will be dividing both age and height with their respective standard deviation
- D. You will be taking square root of height
正解: B,C
解説:
Explanation
When you see the data age in years would have values like 50, 60r 70 90 years etc. And while calculating distance from centroid maximum possible value can be 90-0 and its square will be 8100.
While using heights in meter can be 2-0.5(1.5) meters and its square will be 2.25 only. So you can see age has more effect than height. Hence bringing the height on same level you can convert it into centimeters. Can bring data upto 200 centimeters and then it be more effective like square of 200 maximum.
However there is another approach is to divide the each value with its standard deviation, which will not have impact of the units e.g. age/sd of the age, which results in value without unit. This can also help in reducing the effect of units.
質問 72
You are using k-means clustering to classify heart patients for a hospital. You have chosen Patient Sex, Height, Weight, Age and Income as measures and have used 3 clusters. When you create a pair-wise plot of the clusters, you notice that there is significant overlap between the clusters. What should you do?
- A. Decrease the number of clusters
- B. Increase the number of clusters
- C. Remove one of the measures
- D. Identify additional measures to add to the analysis
正解: A
質問 73
You have used k-means clustering to classify behavior of 100, 000 customers for a retail store. You decide to use household income, age, gender and yearly purchase amount as measures. You have chosen to use 8 clusters and notice that 2 clusters only have 3 customers assigned. What should you do?
- A. Decrease the number of clusters
- B. Increase the number of clusters
- C. Identify additional measures to add to the analysis
- D. Decrease the number of measures used
正解: A
解説:
Explanation
kmeans uses an iterative algorithm that minimizes the sum of distances from each object to its cluster centroid, over all clusters. This algorithm moves objects between clusters until the sum cannot be decreased further. The result is a set of clusters that are as compact and well-separated as possible. You can control the details of the minimization using several optional input parameters to kmeans, including ones for the initial values of the cluster centroids, and for the maximum number of iterations.
Clustering is primarily an exploratory technique to discover hidden structures of the data: possibly as a prelude to more focused analysis or decision processes. Some specific applications of k-means are image processing^ medical and customer segmentation. Clustering is often used as a lead-in to classification. Once the clusters are identified, labels can be applied to each cluster to classify each group based on its characteristics. Marketing and sales groups use k-means to better identify customers who have similar behaviors and spending patterns.
質問 74
If E1 and E2 are two events, how do you represent the conditional probability given that E2 occurs given that E1 has occurred?
- A. P(E1+E2)/P(E1)
- B. P(E2)/P(E1)
- C. P(E2)/(P(E1+E2)
- D. P(E1)/P(E2)
正解: B
質問 75
A denote the event 'student is female' and let B denote the event 'student is French'. In a class of 100 students suppose 60 are French, and suppose that 10 of the French students are females. Find the probability that if I pick a French student, it will be a girl, that is, find P(A|B).
- A. 2/6
- B. 1/3
- C. 2/3
- D. 1/6
正解: D
解説:
Explanation
Since 10 out of 100 students are both French and female, then
P(AandB)=10100
Also. 60 out of the 100 students are French, so
P(B)=60100
So the required probability is:
P(A|B)=P(AandB)P(B)=10/10060/100=16
質問 76
Which of the following technique can be used to the design of recommender systems?
- A. 2 and 3
- B. Collaborative filtering
- C. Power iteration
- D. Naive Bayes classifier
- E. 1 and 3
正解: B
解説:
Explanation
One approach to the design of recommender systems that has seen wide use is collaborative filtering.
Collaborative filtering methods are based on collecting and analyzing a large amount of information on users' behaviors, activities or preferences and predicting what users will like based on their similarity to other users.
A key advantage of the collaborative filtering approach is that it does not rely on machine analyzable content and therefore it is capable of accurately recommending complex items such as movies without requiring an
"understanding" of the item itself. Many algorithms have been used in measuring user similarity or item similarity in recommender systems. For example the k-nearest neighbor (k-NN) approach and the Pearson Correlation
質問 77
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?
- A. Age
- B. Gender
- C. Credit Score
- D. Income
正解: C
質問 78
You are creating a regression model with the input income, education and current debt of a customer, what could be the possible output from this model.
- A. Customer fit as a good
- B. expressed as a percent, that the customer will default on a loan
- C. Customer fit as acceptable or average category
- D. 1 and 3 are correct
- E. 2 and 3 are correct
正解: B
解説:
Explanation
Regression is the process of using several inputs to produce one or more outputs. For example The input might be the income, education and current debt of a customer The output might be the probability, expressed as a percent that the customer will default on a loan. Contrast this to classification where the output is not a number, but a class.
質問 79
Suppose there are three events then which formula must always be equal to P(E1|E2,E3)?
- A. P(E1,E2,E3)P(E1)/P(E2:E3)
- B. P(E1,E2;E3)/P(E2,E3)
- C. P(E1,E2|E3)P(E3)
- D. P(E1,E2|E3)P(E2|E3)P(E3)
- E. P(E1,E2,E3)P(E2)P(E3)
正解: B
解説:
Explanation
This is an application of conditional probability: P(E1,E2)=P(E1|E2)P(E2). so P(E1|E2) = P(E1.E2)/P(E2) P(E1,E2,E3)/P(E2,E3) If the events are A and B respectively, this is said to be "the probability of A given B" It is commonly denoted by P(A|B): or sometimes PB(A). In case that both "A" and "B" are categorical variables, conditional probability table is typically used to represent the conditional probability.
質問 80
Classification and regression are examples of___________.
- A. Density estimation
- B. un-supervised learning
- C. Clustering
- D. supervised learning
正解: D
解説:
Explanation
In classification, our job is to predict what class an instance of data should fall into. Another task in machine learning is regression. Regression is the prediction of a numeric value. Most people have probably seen an example of regression with a best-fit line drawn through some data points to generalize the data points.
Classification and regression are examples of supervised learning. This set of problems is known as supervised because we're telling the algorithm what to predict.
質問 81
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