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This qualifying exam for Data Science Foundations 2023 focuses on the practice of data analytics, the role of the Data Scientist, the main phases of the Data Analytics Lifecycle, analyzing and exploring data with R, statistics for model building and evaluation, the theory and methods of advanced analytics and statistical modeling, the technology and tools that can be used for advanced analytics, operationalizing an analytics project, and data visualization techniques. Successful candidates will achieve the Dell EMC Proven Professional – Data Science Associate credential. The exam has a duration of 90 minutes, consists of 60 questions, and requires a pass score of 60.

Topics likely to be covered on this exam include:

● Define and describe the characteristics of Big Data

● Describe the business drivers for Big Data analytics and data science

● Describe the Data Scientist role and related skills

● Describe the data analytics lifecycle purpose and sequence of phases

● Discovery - Describe details of this phase, including activities and associated roles

● Data preparation - Describe details of this phase, including activities and associated roles

● Model planning - Describe details of this phase, including activities and associated roles

● Model building - Describe details of this phase, including activities and associated roles

● Explain how basic R commands are used to initially explore and analyze the data

● Describe and provide examples of the most important statistical measures and effective visualizations of data

● Describe the theory, process, and analysis of results for hypothesis testing and its use in evaluating a model

Describe theory, application, and interpretation of results for the following methods:

● K-means clustering

● Association rules

● Linear regression

● Logistic Regression

● Na?ve Bayesian classifiers

● Decision trees

● Time Series Analysis

● Text Analytics

● Describe the technological challenges posed by Big Data

● Describe the nature and use of MapReduce and Apache Hadoop

● Describe the Hadoop ecosystem and related product use cases

● Describe in-database analytics and SQL essentials

● Describe advanced SQL methods: window functions, ordered aggregates, and MADlib

● Describe best practices for communicating findings and operationalizing an analytics project

● Describe best practices for building project presentations for specific audiences

● Describe best practices for planning and creating effective data visualizations

1. What is an appropriate assignment for a data scientist?

A. Monitor key performance indicators

B. Define an OLAP database schema

C. Conduct customer surveys

D. Develop predictive models

Answer: D

2. What is the output format from the Map function of MapReduce?

A. Key-value pairs

B. Binary representation of keys concatenated with structured data

C. Compressed index

D. Unique key record and separate records of all possible values

Answer: A

3. During the data preparation phase, you notice a high correlation between average spend on video games, age of players, and number of science fiction shows watched.

Which technique could you use to address the three correlated variables?

A. Square the three variables to remove the correlation

B. Combine the three variables into one new variable

C. Drop the three variables to improve the model

D. Use scaling to make the three variables equivalent in size

Answer: B

4. What are considerations in a data science and Big Data analytics project?

A. Ignoring executive stakeholders and business users

B. Applying the latest technologies to demonstrate technical skills

C. Analysis flexibility and decision making

D. Building data silos and bypassing data privacy rules

Answer: C

5. What is the primary function of the NameNode in Hadoop?

A. Keeps track of which MapReduce jobs have been assigned to each TaskTracker

B. Monitors the state of each JobTracker node and signals an event if unavailable

C. Runs some number of mapping tasks against its assigned data

D. Acts as a regulator/resolver among clients and DataNodes

Answer: D

6. What activities occur during the discovery phase of the data analytics lifecycle?

A. Deploy and monitor model performance

B. Build training and test datasets

C. Interview project sponsor and stakeholders

D. Perform ETL and data exploration

Answer: C

7. What is a motivation for using a data analytics lifecycle?

A. Explores all possible approaches

B. Limits the amount of data needed

C. Guarantees a successful project

D. Creates a repeatable process

Answer: D

8. What is a key consideration when preparing a presentation intended for sponsors?

A. Describe how current processes may be affected

B. Provide details on model planning and building

C. Describe how to implement the model

D. Emphasize the business benefits of implementing the model

Answer: D

9. Which characteristic applies only to Business Intelligence as opposed to Data Science?

A. Uses only structured data

B. Supports solving “what if” scenarios

C. Uses large data sets

D. Uses predictive modeling techniques

Answer: A

10. In which phase of the data analytics lifecycle do Data Scientists spend the most time in a project?

A. Discovery

B. Data Preparation

C. Model Building

D. Communicate Results

Answer: B