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Updated EMCD-DS-FN-23 Exam Questions in PDF Format for Quick Preparation

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>> D-DS-FN-23 Exam Syllabus <<

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

Topic
Details

Topic 1

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 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

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.

EMC Dell Data Science Foundations Sample Questions (Q253-Q258):

NEW QUESTION # 253
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. {soda} => {milk}
B. {cheese} => {bread}
C. {juice} => {cheese}
D. {milk} => {soda}

Answer: B

NEW QUESTION # 254
A logistic regression model is built to determine the probability of a credit card borrower defaulting on a credit loan. A threshold value of 0.3 is selected. Which statement can be used to predict a borrower will default?

A. If probability > 0.3, then predict the borrower will default
B. If probability > 0.1, then predict the borrower will default
C. If probability < 0.3, then predict the borrower will default
D. If probability < 0.1, then predict the borrower will default

Answer: A

Explanation:
In logistic regression, the threshold determines the cutoff for classifying an outcome. If the probability exceeds the threshold (0.3 in this case), the model predicts the positive class-here, that the borrower will default.

NEW QUESTION # 255
What is a benefit of Spark in-memory data processing as opposed to using MapReduce?

A. Supports processing unstructured data, which MapReduce does not allow
B. Avoids writing intermediate data to disk, which speeds up processing
C. Removes the need to use disks at all, which reduces cost
D. Allows parallel processing, which MapReduce does not support

Answer: B

Explanation:
Spark's in-memory data processing avoids the need to write intermediate data to disk, which significantly speeds up processing compared to MapReduce, which writes intermediate data to disk after each operation.
This makes Spark much faster for iterative tasks.

NEW QUESTION # 256
Assume that you have a data frame in R.
Which function would you use to display descriptive statistics about this variable?

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

Answer: B

NEW QUESTION # 257
You are using the Apriori algorithm to determine the likelihood that a person who owns a home has a good credit score. You have determined that the confidence for the rules used in the algorithm is > 75%. You calculate lift = 1.011 for the rule, "People with good credit are homeowners".
What can you determine from the lift calculation?

A. The rule is true
B. Leverage of the rules is low
C. The rule is coincidental
D. Support for the association is low

Answer: C

NEW QUESTION # 258
......

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