DEA-7TT2 無料問題集「EMC Associate - Data Science and Big Data Analytics v2」

（A）Goal 1 and 3

（B）Goals 1, 2, 3, 4

（C）Goals 2, 3, 4

（D）Goal 2 and 4

（A）Create interaction variables based only on variables A, B, and C

（B）Create clusters based on the data and use them as model inputs

（C）Break variables A, B, and C into their own univariate models

（D）Force all 15 variables into the model as independent variables

（A）K-means is to Naive Bayes

（B）Confusion matrix is to classifier

（C）Data scientist is to big data

（D）Seasonality is to ARIMA

（A）Remove one of the measures

（B）Increase the number of clusters

（C）Identify additional measures to add to the analysis

（D）Decrease the number of clusters

（A）Uses large data sets

（B）Uses only structured data

（C）Supports solving "what if" scenarios

（D）Uses predictive modeling techniques

（A）The change in purchase size is not practically important, and the good p-value of the second study is probably a result of the large study size.

（B）The change in purchase size is small, but may aggregate up to a large increase in profits over the entire customer base.

（C）The difference in the change in purchase size between the two studies is troubling; The team should run another, larger study.

（D）The p-value of the second study shows a statistically significant change in purchase size. The new website is an improvement.

（A）The tree did not split on all the input variables. You need a larger data set to get a more accurate model.

（B）The AUC is high, so the overall model is accurate. It is not well-calibrated, because the small nodes will give poor estimates of probability.

（C）The tree is probably overfit. Try fitting shallower trees and using an ensemble method.

（D）The AUC is high, and the small nodes are all very pure. This is an accurate model.

（A）Rows retain their separate identities and the window function can access more than the current row.

（B）Rows are grouped into an output row and the window function can access more than the current row.

（C）Rows retain their separate identities and the window function can only access the current row.

（D）Rows are grouped into an output row and the window function can only access the current row.

（A）more efficient handling of categorical values

（B）improved connections between disparate data sources

（C）full use of data aggregation functionality

（D）quicker time to insight

（A）Outcome of a test on a variable

（B）Root of the decision tree

（C）Decision point on a variable

（D）Assigned class label

（A）Precision = 277/262 Recall = 288/262

（B）Precision = 262/277 Recall = 262/288

（C）Precision = 288/262 Recall = 277/262

（D）Precision =262/288 Recall = 262/277

（A）Real

（B）Ordinal

（C）Ratio

（D）Nominal

（A）Fig-C

（B）Fig-D

（C）Fig-B

（D）Fig-A

（A）The R-squared may be biased upwards by the extreme-valued outcomes. Remove them and refit to get a better idea of the model,s quality over typical data.

（B）The R-squared is good. The model should perform well.

（C）The extreme-valued outliers may negatively affect the model,s performance. Remove them to see if the R-squared improves over typical data.

（D）The observations seem to come from two different populations, but this model fits them both equally well.

（A）List

（B）Array

（C）Data frame

（D）Matrix

（A）Curious and creative

（B）Proficient in Microsoft Project and Excel

（C）Project management and administrative skills

（D）Accounting and regulatory skills

（A）Multicollinearity is to OLS

（B）Multicollinearity is to serial correlation

（C）Confidence interval is to regression

（D）Confidence is to leverage

（A）Cannot handle variables that affect the outcome in a discontinuous way

（B）Correlated variables can cause double-counting

（C）Short decision trees are likely subject to overfit

（D）Tree structure is sensitive to small changes in the training data