Dell EMC Data Science Associate Certification Questions and Answers (Dumps and Practice Questions)

Question :

Logistic regression is a model used for prediction of the probability of occurrence of an event.
It makes use of several variables that may be___________

1. Numerical
2. Categorical
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4. None of the 1 and 2 are correct



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Explanation: 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.





Question : Select the correct statement regarding the naive Bayes classification

1. it only requires a small amount of training data to estimate the parameters
2. Independent variables can be assumed
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4. for each class entire covariance matrix need to be determined


1. 1,2,3
2. 2,3,4
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4. 2,3,4


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Explanation: An advantage of naive Bayes is that it only requires a small amount of training data to estimate the parameters (means and variances of the variables) necessary for classification. Because independent variables are assumed, only the variances of the variables for each class need to be determined and not the entire covariance matrix.





Question : Spam filtering of the emails is an example of

1. Supervised learning
2. Unsupervised learning
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4. 1 and 3 are correct
5. 2 and 3 are correct

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Explanation: Clustering is an example of unsupervised learning. The clustering algorithm finds groups within the data without being told what to look for upfront. This contrasts with classification, an example of supervised machine learning, which is the process of determining to which class an observation belongs. A common application of classification is spam filtering. With spam filtering we use labeled data to train the classifier: e-mails marked as spam or ham.

Related Questions

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Question : In which lifecycle stage are appropriate analytical techniques determined?

1. Model planning
2. Model building
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4. Discovery



Question : What is Hadoop?

1. Java classes for HDFS types and MapReduce job management and HDFS
2. Java classes for HDFS types and MapReduce job management and the MapReduce paradigm
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4. MapReduce paradigm and massive unstructured data storage on commodity hardware





Question : 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?



1. Decrease the number of clusters
2. Increase the number of clusters
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4. Identify additional measures to add to the analysis





Question : How does Pig's use of a schema differ from that of a traditional RDBMS?



1. Pig's schema requires that the data is physically present when the schema is defined
2. Pig's schema is required for ETL
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4. Pig's schema is optional






Question : You are provided four different datasets. Initial analysis on these datasets show that they have
identical mean, variance and correlation values. What should your next step in the analysis be?


1. Select one of the four datasets and begin planning and building a model
2. Combine the data from all four of the datasets and begin planning and bulding a model
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4. Visualize the data to further explore the characteristics of each data set




Question : You are asked to create a model to predict the total number of monthly subscribers for a specific
magazine. You are provided with 1 year's worth of subscription and payment data, user
demographic data, and 10 years worth of content of the magazine (articles and pictures). Which
algorithm is the most appropriate for building a predictive model for subscribers?


1. TF-IDF
2. Linear regression
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4. Decision trees