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[General] D-DS-FN-23 Exam Torrent & D-DS-FN-23 Exam Preparation & D-DS-FN-23 Test

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【General】 D-DS-FN-23 Exam Torrent & D-DS-FN-23 Exam Preparation & D-DS-FN-23 Test

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EMC D-DS-FN-23 Exam Syllabus Topics:
TopicDetails
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
  • 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 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.
Topic 4
  • 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.

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EMC Dell Data Science Foundations Sample Questions (Q16-Q21):NEW QUESTION # 16
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 tabluated them as shown in the exhibit.
What are the Precision and Recall rate of the model?
  • A. Precision = 288/262 Recall = 277/262
  • B. Precision =262/288 Recall = 262/277
  • C. Precision = 277/262 Recall = 288/262
  • D. Precision = 262/277 Recall = 262/288
Answer: D

NEW QUESTION # 17
Before you build an ARMA model, how can you tell if your time series is weakly stationary?
  • A. The mean of the series is close to 0.
  • B. The series is normally distributed.
  • C. There appears to be no apparent trend component.
  • D. There appears to be a constant variance around a constant mean.
Answer: D

NEW QUESTION # 18
Review the following code:
SELECT pn, vn, sum(prc*qty) FROM sale
GROUP BY CUBE(pn, vn) ORDER BY 1, 2, 3;
Which combination of subtotals do you expect to be returned by the query?
  • A. ( (pn, vn) , (pn), (vn) )
  • B. ( (pn, vn), (pn) )
  • C. (pn, vn)
  • D. ( (pn, vn) , (pn), (vn) , ( ) )
Answer: D

NEW QUESTION # 19
In R, functions like plot() and hist() are known as what?
  • A. generic methods
  • B. generic functions
  • C. virtual methods
  • D. virtual functions
Answer: C

NEW QUESTION # 20
You are using k-means clustering to classify heart patients for a hospital. You have chosen Patient Sex, Height, Weight, Age and Income as measures and have used 3 clusters.
When you create a pair-wise plot of the clusters, you notice that there is significant overlap between the clusters.
What should you do?
  • A. Identify additional measures to add to the analysis
  • B. Increase the number of clusters
  • C. Decrease the number of clusters
  • D. Remove one of the measures
Answer: C

NEW QUESTION # 21
......
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