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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	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 3	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 4	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 5	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 (Q54-Q59):

NEW QUESTION # 54
Which SQL OLAP extension provides all possible grouping combinations?

A. UNION ALL
B. CROSS JOIN
C. ROLLUP
D. CUBE

Answer: D

NEW QUESTION # 55
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. Add more storage to the spam filtering system
B. Retrain the spam filter with newer examples of spam emails
C. Add more processing power to the spam filtering system
D. Create a linear regression model to calculate the probability of an email being spam

Answer: B

NEW QUESTION # 56
After which phase of the data analytics lifecycle should you determine if the model is robust enough?

A. Discovery
B. Model building
C. Data preparation
D. Operationalize

Answer: B

NEW QUESTION # 57
Based on the exhibit, what is a likely issue with the data?

A. No obvious concerns with the data is visible
B. Saturated data; indicating potential issues with data definitions
C. Incomplete data; indicating potential issues with data transmission
D. Mis-scaled data; indicating potential issues with data entry

Answer: B

NEW QUESTION # 58
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 = 15/262 FNR = 26/288
B. FPR = 26/288 FNR = 15/262
C. FPR = 262/15 FNR = 288/26
D. FPR = 288/26 FNR = 262/15

Answer: A

NEW QUESTION # 59
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