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

Topic
Details

Topic 1

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 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

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.

Topic 4

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.

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EMC Dell Data Science Foundations Sample Questions (Q40-Q45):

NEW QUESTION # 40
What is a distinct property of Logistic Regression compared with Linear Regression?

A. Logistic Regression works well with discrete variables that have many distinct values
B. Logistic Regression handles missing values well
C. Logistic Regression is robust with redundant or correlated variables
D. Logistic Regression returns probability estimates of an event

Answer: D

NEW QUESTION # 41
Refer to the exhibit.

You ran a linear regression, and the final output is seen in the exhibit. Based only on the information in the exhibit and an acceptable confidence level of 95%, how would you interpret the interaction of variable D with the dependent variable?

A. For every 1 unit increase in variable D, holding all other variables constant, we can expect the dependent variable to be multiplied by 10.23 units
B. In this model, Variable D is not significantly interacting with the dependent variable
C. Variable D is more significant than variables A, B, and C.
D. For every 1 unit increase in variable D, holding all other variables constant, we can expect the dependent variable to increase by 10.23 units

Answer: B

NEW QUESTION # 42
What is part of the model output for a linear regression?

A. Probability score for each possible class label
B. Coefficients indicating relative impact of the input variables on the outcome
C. The set of all rules X -> Y with minimum support and confidence
D. The assignment of each input datum to a cluster

Answer: B

Explanation:
In linear regression, the model output includes coefficients that indicate the relative impact of the input variables on the outcome. These coefficients represent how much each input variable influences the predicted outcome.

NEW QUESTION # 43
In association rules, given X -> Y, what is confidence?

A. How many times more often X and Y occur together than expected if they were statistically independent, expressed as a ratio
B. Percentage of transactions that contain the itemset
C. Difference in the probability of X and Y appearing together compared with expectations if they were statistically independent
D. Percentage of transactions with X that also contain Y

Answer: D

NEW QUESTION # 44
During a study to understand the population growth of a certain bacterial culture, you plot the data and identify a quadratic growth trend over time.
Which transformation should you apply to linearize the data?

A. Add a constant
B. Cube
C. Square root
D. Square

Answer: C

NEW QUESTION # 45
......

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