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

Topic	Details
Topic 1	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 2	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.
Topic 3	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.

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With the Dell Data Science Foundations D-DS-FN-23 exam, you will have the chance to update your knowledge while obtaining dependable evidence of your proficiency. You can benefit from a number of additional benefits after completing the Dell Data Science Foundations D-DS-FN-23 Certification Exam. But keep in mind that the D-DS-FN-23 certification test is a worthwhile and challenging certificate.

EMC Dell Data Science Foundations Sample Questions (Q96-Q101):

NEW QUESTION # 96
An IT department deployed a spam filter to reduce the amount of junk e-mail received by its employees.
After six months, they notice that the spam filter is less effective than when initially deployed.
They examine the system running the spam filter and it appears to be operating normally.
What action would improve the effectiveness of the spam filter?

A. Retrain the spam filter with newer examples of spam emails
B. Add more storage to the spam filtering system
C. Create a linear regression model to calculate the probability of an email being spam
D. Add more processing power to the spam filtering system

Answer: A

NEW QUESTION # 97
What describes a true limitation of a Logistic Regression method?

A. Does not have explanatory values
B. Does not handle missing values well
C. Does not handle correlated variables well
D. Does not handle redundant variables well

Answer: B

NEW QUESTION # 98
Refer to the exhibit.

You have scored your Naive bayesian classifier model on a hold out test data for cross validation and determined the way the samples scored and tabulated them as shown in the exhibit.
What are the the False Positive Rate (FPR) and the False Negative Rate (FNR) of the model?

A. FPR = 26/288 FNR = 15/262
B. FPR = 15/262 FNR = 26/288
C. FPR = 288/26 FNR = 262/15
D. FPR = 262/15 FNR = 288/26

Answer: B

NEW QUESTION # 99
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 naïve Bayes classification model.
Inthemodel, therearetwo classlabels, renewaland non-renewal, thatare assignedtoeachcustomerbased on their attributes. Asubset 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 their policies every year, what is the score for a non-renewal in the naïve Bayesian model for the customer described above?

A. 0.0004
B. 0.0040
C. 0.0020
D. 0.0002

Answer: A

NEW QUESTION # 100
Consider a database with 4 transactions:
Transaction 1: {cheese, bread, milk}
Transaction 2: {soda, bread, milk}
Transaction 3: {cheese, bread}
Transaction 4: {cheese, soda, juice}
You decide to run the association rules algorithm where minimum support is 50%.
Which rule has a confidence at least 50%?

A. {juice} => {cheese}
B. {milk} => {soda}
C. {soda} => {milk}
D. {cheese} => {bread}

Answer: D

NEW QUESTION # 101
......

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