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Title: Ace Your Exam Preparation with TestKingFree D-DS-FN-23 Practice Test [Print This Page]
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Title: Ace Your Exam Preparation with TestKingFree D-DS-FN-23 Practice Test
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EMC D-DS-FN-23 Exam Syllabus Topics:
TopicDetails
Topic 1
  • Operationalizing an Analytics Project and Data Visualization Techniques: This section of the exam measures the skills of an Entry-Level Data Analyst and explains best practices for communicating findings and operationalizing analytics projects. It covers effective methods for presenting projects to various audiences and emphasizes the importance of planning and creating impactful data visualizations.
Topic 2
  • Big Data, Analytics, and the Data Scientist Role: This section of the exam measures the skills of a Data Science Enthusiast and covers the basic concepts of Big Data, including its defining characteristics and the business drivers behind its rise. It also introduces the role of the Data Scientist, highlighting the critical skills needed in the data science field.
Topic 3
  • Initial Analysis of the Data: This section of the exam measures the skills of a Data Science Enthusiast and focuses on the first steps in analyzing data. It explains how basic R commands are used for exploration, discusses important statistical measures and visualizations, and describes hypothesis testing techniques for evaluating models.

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EMC Dell Data Science Foundations Sample Questions (Q40-Q45):NEW QUESTION # 40
Refer to the exhibit.

You are using k-means clustering to discover groupings within a data set. You plot within- sum-of-squares (wss) of multiple cluster sizes.
Based on the exhibit, how many clusters should you use in your analysis?
Answer: D

NEW QUESTION # 41
What is the purpose of applying the naive Bayes conditional independence assumption?
Answer: B
Explanation:
The naive Bayes conditional independence assumption is applied to simplify the probability calculations by assuming that the features are independent given the class label. This reduces the complexity of the model and makes computation feasible even with many features.

NEW QUESTION # 42
You are studying the behavior of a population, and you are provided with multidimensional data at the individual level. You have identified four specific individuals who are valuable to your study, and would like to find all users who are most similar to each individual.
Which algorithm is the most appropriate for this study?
Answer: A

NEW QUESTION # 43
What does R code nv <- v[v < 1000] do?
Answer: D

NEW QUESTION # 44

Refer to the exhibit.
To predict whether or not a customer will renew their annual property insurance policy, an insurance company built and operationalized a naive Bayes classification model. In the model, there are two class labels, renewal and non-renewal, that are assigned to each customer based on their attributes.
A subset of the key attributes, their values, and corresponding conditional probabilities are provided in the exhibit.
A customer has the following attributes:
# Age is greater than 65 years
# Owns their own home
# Renewal month is August
If 20% of customers do not renew the police every year, what is the score for a renewal in the naive Bayesian model for the customer described above?
Answer: D
Explanation:
The formula for Naive Bayes is:

For the renewal class, we are given:
# P(Class = Renewal) = 0.8 (since 80% renew the policy)
# P(Age > 65 years | Renewal) = 0.3
# P(Housing = Own | Renewal) = 0.9
# P(Renewal Month = August | Renewal) = 0.1
P(Renewal) = P(Renewal) ¡Á P(Age > 65 years | Renewal) ¡Á P(Housing = Own | Renewal) ¡Á P (Renewal Month = August | Renewal) P(Renewal) = 0.8 ¡Á 0.3 ¡Á 0.9 ¡Á 0.1 = 0.0216

NEW QUESTION # 45
......
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