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NEW QUESTION: 1
The CUSTOMERS table has 55,500 rows and 620 distinct values in the CUST_CITY_ID column. The number of popular values is 54 and less than 99% of the rows contain popular values.
Which type of histogram should you create to accurately determine the cardinality estimate on the CUST_CITY_ID column?
A. high-frequency histogram
B. frequency histogram
C. hybrid histogram
D. height-balanced histogram
Answer: D
NEW QUESTION: 2
A. Option B
B. Option C
C. Option D
D. Option A
Answer: A
NEW QUESTION: 3
You need to implement a feature engineering strategy for the crowd sentiment local models.
What should you do?
A. Apply a Spearman correlation coefficient.
B. Apply a linear discriminant analysis.
C. Apply a Pearson correlation coefficient.
D. Apply an analysis of variance (ANOVA).
Answer: B
Explanation:
Explanation
The linear discriminant analysis method works only on continuous variables, not categorical or ordinal variables.
Linear discriminant analysis is similar to analysis of variance (ANOVA) in that it works by comparing the means of the variables.
Scenario:
Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
Experiments for local crowd sentiment models must combine local penalty detection data.
All shared features for local models are continuous variables.
NEW QUESTION: 4
Which of the following load-balancing methods is not valid?
A. Round trip
B. Domain
C. They are all valid
D. Random
Answer: C