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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

• 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 - 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 4

• 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 5

• 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.

 

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D-DS-FN-23 Training Materials: Dell Data Science Foundations & D-DS-FN-23 Exam Preparatory

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EMC Dell Data Science Foundations Sample Questions (Q267-Q272):

NEW QUESTION # 267
Which chart type is intended to display time series data?

• A. Pie chart
• B. Line chart
• C. [Histogram
• D. Bar chart

Answer: B

Explanation:
A line chart is specifically designed to display time series data. It shows data points in a sequential order, making it easy to observe trends over time.

 

NEW QUESTION # 268
In MADlib what does MAD stand for?

• A. Machine Learning, Algorithms for Databases
• B. Magnetic, Agile, Deep
• C. Modular, Accurate, Dependable
• D. Mathematical Algorithms for Databases

Answer: B

 

NEW QUESTION # 269
You have created a Linear Regression model to predict total sales based on variables M, N, P and Q as shown in the graphic. You originally expected all variables to have positive coefficients.

Which action would you take?

• A. Accept none of the variables and investigate correlations between all variables
• B. Accept only positive variables and investigate potential correlation with the dependent variable
• C. Accept all variables and begin model validation steps against holdout data
• D. Accept only statistically significant variables and investigate correlated independent variables

Answer: A

 

NEW QUESTION # 270
Which activity might be performed in the Operationalize phase of the Data Analytics Lifecycle?

• A. Transform existing variables
• B. Run a pilot
• C. Try different variables
• D. Try different analytical techniques

Answer: B

 

NEW QUESTION # 271
Refer to the exhibit.

In the exhibit, the x-axis represents the derived probability of a borrower defaulting on a loan. Also in the exhibit, the pink represents borrowers that are known to have not defaulted on their loan, and the blue represents borrowers that are known to have defaulted on their loan.
Which analytical method could produce the probabilities needed to build this exhibit?

• A. Association Rules
• B. Linear Regression
• C. Logistic Regression
• D. Discriminant Analysis

Answer: C

 

NEW QUESTION # 272
......

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