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EMC D-DS-FN-23 Exam Syllabus Topics:

Topic	Details
Topic 1	Data Analytics Lifecycle: This section of the exam measures the skills of an Entry-Level Data Analyst and explains the purpose and phases of the data analytics lifecycle. It includes understanding key activities and roles involved in the discovery, data preparation, model planning, and model building phases to successfully manage analytics projects.
Topic 2	Advanced Analytics for Big Data - Technology and Tools: This section of the exam measures the skills of a Data Science Enthusiast and addresses the technological challenges associated with Big Data. It introduces tools and technologies such as MapReduce, Hadoop, the Hadoop ecosystem, in-database analytics, SQL essentials, and advanced SQL techniques like window functions and MADlib.
Topic 3	Advanced Analytics - Theory, Application, and Interpretation of Results for Eight Methods: This section of the exam measures the skills of an Entry-Level Data Analyst and covers foundational knowledge in various advanced analytics methods. Topics include the theory, application, and interpretation of K-means clustering, association rules, linear and logistic regression, naïve Bayesian classifiers, decision trees, time series analysis, and text analytics.

EMC Dell Data Science Foundations Sample Questions (Q51-Q56):

NEW QUESTION # 51
In addition to less data movement and the ability to use larger datasets in calculations, what is a benefit of analytical calculations in a database?

A. more efficient handling of categorical values
B. quicker time to insight
C. improved connections between disparate data sources
D. full use of data aggregation functionality

Answer: B

NEW QUESTION # 52
Assume that you have a data frame in R.
Which function would you use to display descriptive statistics about this variable?

A. str
B. levels
C. summary
D. attributes

Answer: C

NEW QUESTION # 53
Why do the Naïve Bayesian classifier implementations use the log of probability value rather than the pure probability value?

A. To avoid numerical underflow errors in high dimensional problems
B. To ensure the conditional independence of attribute values
C. To obtain a more accurate estimate of the probabilities without the need for a Laplace smoothing
D. To invalidate the variables that are continuous

Answer: A

NEW QUESTION # 54
Consider a database with the following four transactions:
Transaction 1: {cheese, bread, milk}
Transaction 2: {soda, bread, milk}
Transaction 3: {cheese, bread}
Transaction 4: {cheese, soda, juice}
Assuming a minimum support of 50%, which itemsets would be generated in the second step of the Apriori algorithm?

A. {cheese}, {bread}, {milk}, {soda}
B. {cheese, bread}, {bread, milk}
C. {cheese, bread}, {cheese, milk}, {bread, milk}
D. {cheese, bread}, {cheese, bread, milk}

Answer: B

NEW QUESTION # 55
What is a business driver for Big Data analytics adoption?

A. Implement the latest technology and tools
B. Ensure the analysts work in isolation
C. Identify new business opportunities
D. Maintain existing data silos

Answer: C

Explanation:
A key business driver for Big Data analytics adoption is the ability to identify new business opportunities by analyzing large and complex datasets, which can provide valuable insights for decision-making and strategy development.

NEW QUESTION # 56
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2025 D-DS-FN-23: Dell Data Science Foundations Marvelous Reliable Study Questions

Pass4sure D-DS-FN-23 Pass Guide - D-DS-FN-23 Simulations Pdf

EMC D-DS-FN-23 Exam Syllabus Topics:

EMC Dell Data Science Foundations Sample Questions (Q51-Q56):

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