The D-DS-FN-23 Dell Data Science Foundations 2023 exam is an essential certification for anyone looking to build a career in data science. Using PassQuestion D-DS-FN-23 Dell Data Science Foundations Exam Questions is one of the most effective ways to test your knowledge. These questions simulate the real exam environment, allowing you to get a feel for the types of questions and the level of difficulty. With the latest D-DS-FN-23 Dell Data Science Foundations Exam Questions from PassQuestion, candidates can access a treasure trove of practice material designed to help them pass their exams with ease. 

Dell Data Science Foundations D-DS-FN-23 Overview

The Dell Data Science Foundations D-DS-FN-23 exam is designed for individuals who want to demonstrate their understanding of the core concepts of data science and analytics. This exam validates foundational knowledge and practical skills, equipping candidates to actively participate in data analytics projects. It is an ideal starting point for those pursuing careers in big data and data science.

The exam assesses various topics including the data analytics lifecycle, data visualization, statistical modeling, and the tools and technologies used in advanced analytics. Successful candidates will have demonstrated practical competence in analyzing and exploring data with R, understanding key statistical concepts for model building and evaluation, and applying data science methods in real-world contexts.

Certification Requirements

To successfully achieve the D-DS-FN-23 Dell Data Science Foundations Certification, candidates must meet the following criteria:

• Sufficient Knowledge Base: Candidates should possess a strong foundational knowledge, either from hands-on product experience or by engaging with Dell's recommended training materials.
• Pass the Dell Data Science Foundations Exam: Candidates must successfully pass the D-DS-FN-23 exam, which tests knowledge across several key areas of data science.

Exam Topics Covered in the D-DS-FN-23 Certification

The exam consists of various topics that assess your theoretical understanding and practical knowledge of data science. Below is a breakdown of the key exam topics:

Big Data, Analytics, and the Data Scientist Role (5%)

• 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

Data Analytics Lifecycle (8%)

• 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

Initial Analysis of the Data (15%)

• 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

Advanced Analytics - Theory, Application, and Interpretation of Results for Eight Methods (40%)

Describe theory, application, and interpretation of results for the following methods:
• K-means clustering
• Association rules
• Linear regression
• Logistic Regression
• Naïve Bayesian classifiers
• Decision trees
• Time Series Analysis
• Text Analytics

Advanced Analytics for Big Data - Technology and Tools (22%)

• 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

Operationalizing an Analytics Project and Data Visualization Techniques (10%)

• 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

Preparing for the Exam: Study Strategies and Resources

Preparation is key to passing the D-DS-FN-23 exam. This section offers useful tips on how to approach your study sessions and maximize your chances of success.
Study Tips Include:

• Understand the Exam Format: Familiarize yourself with the structure of the exam, including the number of questions, the time limit, and the types of questions (multiple-choice, scenario-based, etc.).
• Review Core Concepts: Focus on mastering the key concepts, such as data analytics lifecycle phases, advanced analytics methods, and Big Data technologies.
• Hands-on Practice: Try to gain hands-on experience by working on real-world data sets. Use tools like R and Python to practice building models and conducting data analyses.
• Practice Online Sample Test: Use the Practice Exam to practice answering exam-like questions. This will help you familiarize yourself with the question format and assess your knowledge.

View Online Dell Data Science Foundations D-DS-FN-23 Free Questions

1. In the Map Reduce framework, what is the purpose of the Reduce function?
A. It aggregates the results of the Map function and generates processed output
B. It distributes the input to multiple nodes for processing
C. It writes the output of the Map function to storage
D. It breaks the input into smaller components and distributes to other nodes in the cluster
Answer: A
 
2. What is an example of a null hypothesis?
A. that a newly created model provides a prediction of a null sample mean
B. that a newly created model provides a prediction of a null population mean
C. that a newly created model does not provide better predictions than the currently existing model
D. that a newly created model provides a prediction that will be well fit to the null distribution
Answer: C
 
3. You submit a Map Reduce job to a Hadoop cluster. However, you notice that although the job was successfully submitted, it is not completing.
What should be done to identify the issue?
A. Ensure DataNode is running
B. Ensure NameNode is running
C. Ensure JobTracker is running
D. Ensure TaskTracker is running
Answer: D
 
4. How are window functions different from regular aggregate functions?
A. Rows retain their separate identities and the window function can access more than the current row.
B. Rows are grouped into an output row and the window function can access more than the current row.
C. Rows retain their separate identities and the window function can only access the current row.
D. Rows are grouped into an output row and the window function can only access the current row.
Answer: A
 
5. Your colleague, who is new to Hadoop, approaches you with a question. They want to know how best to access their data. This colleague has a strong background in data flow languages and programming.
Which query interface would you recommend?
A. Hive
B. Pig
C. HBase
D. Howl
Answer: B
 
6. 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. There appears to be a constant variance around a constant mean.
C. The series is normally distributed.
D. There appears to be no apparent trend component.
Answer: B
 
7. You submit a MapReduce job to a Hadoop cluster and notice that although the job was successfully submitted, it is not completing. What should you do?
A. Ensure that the TaskTracker is running.
B. Ensure that the JobTracker is running
C. Ensure that the NameNode is running
D. Ensure that a DataNode is running
Answer: A
 
8. How does Pig’s use of a schema differ from that of a traditional RDBMS?
A. Pig's schema requires that the data is physically present when the schema is defined
B. Pig's schema supports a single data type
C. Pig's schema is optional
D. Pig's schema is required for ETL
Answer: C
 
9. 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
 
10. For which class of problem is Map Reduce most suitable?
A. Embarrassingly parallel
B. Minimal result data
C. Simple marginalization tasks
D. Non-overlapping queries
Answer: A