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Reliable D-DS-FN-23 Exam Camp | D-DS-FN-23 Vce Format

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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

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

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 4

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 5

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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EMC Dell Data Science Foundations Sample Questions (Q229-Q234):

NEW QUESTION # 229
Which data asset is an example of quasi-structured data?

A. Webserver log
B. XML data file
C. Database table
D. News article

Answer: A

NEW QUESTION # 230
Refer to the Exhibit.

You are working on creating an OLAP query that outputs several rows of with summary rows of subtotals and grand totals in addition to regular rows that may contain NULL as shown in the exhibit.
Which function can you use in your query to distinguish the row from a regular row to a subtotal row?

A. GROUPING
B. ROLLUP
C. RANK
D. GROUP_ID

Answer: A

NEW QUESTION # 231
What is a key consideration when preparing a presentation intended for analysts?

A. Emphasize the business benefits of implementing the model
B. Describe how to implement the model
C. Provide talking points to promote or evangelize the project
D. Focus on clean simple-to-understand visuals

Answer: D

Explanation:
Analysts value clarity and interpretability in data presentations. Clean, simple-to-understand visuals help them accurately assess the data, model outputs, and insights without unnecessary complexity.

NEW QUESTION # 232
Refer to the Exhibit.

In the Exhibit. For effective visualization, what is the chart's primary flaw?

A. The slanting of axis labels.
B. The order of the columns.
C. The use of 3 dimensions.
D. The location of the legend.

Answer: C

NEW QUESTION # 233
A data scientist is given an R data frame, "empdata", with the columns Age, Salary, Occupation, Education, and Gender. The data scientist would like to examine only the Salary and Occupation columns for ages greater than 40.
Which command extracts the appropriate rows and columns from the data frame?

A. empdata[Age > 40, ("Salary", "Occupation")]
B. empdata[, c("Salary", "Occupation")]$Age > 40
C. empdata[empdata$Age > 40, c("Salary", "Occupation")]
D. empdata[c("Salary", "Occupation"), empdata$Age > 40]

Answer: C

NEW QUESTION # 234
......

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