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Reliable EMC Test D-DS-FN-23 Dump & The Best PassSureExam - Leading Provider in Qualification Exams

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

Topic
Details

Topic 1

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 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

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 4

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.

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EMC Dell Data Science Foundations Sample Questions (Q125-Q130):

NEW QUESTION # 125
Consider the example of an analysis for fraud detection on credit card usage. You will need to ensure higher-risk transactions that may indicate fraudulent credit card activity are retained in your data for analysis, and not dropped as outliers during pre-processing.
What will be your approach for loading data into the analytical sandbox for this analysis?

A. OLTP
B. EDW
C. ELT
D. ETL

Answer: C

NEW QUESTION # 126
What describes a true property of Logistic Regression method?

A. It works well with variables that affect the outcome in a discontinuous way.
B. It works well with discrete variables that have many distinct values.
C. It is robust with redundant variables and correlated variables.
D. It handles missing values well.

Answer: C

NEW QUESTION # 127
What is LOESS used for?

A. It is run after a one-way ANOVA, to determine which population has the highest mean value.
B. It plots a continuous variable versus a discrete variable, to compare distributions across classes.
C. It fits a smoothed curve to scatterplot data, to give a general sense of the data's behavior.
D. It is a significance test for the correlation between two variables.

Answer: C

NEW QUESTION # 128
What is a recommended use case for regular expressions?

A. Decision trees
B. Linear regression
C. Logistic regression
D. In-database text analysis

Answer: D

Explanation:
Regular expressions are highly useful for in-database text analysis, where they can be used to search, match, and manipulate text data efficiently within databases, especially for tasks like pattern matching, data extraction, and validation.

NEW QUESTION # 129
Refer to the exhibit.

In association rules, for itemsets X and Y, which expression defines leverage?

A. b
B. a
C. c
D. d

Answer: B

NEW QUESTION # 130
......

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