You can see it againUnder pressure people bring back shapes and positions long after the wording has gone.
Picture superiority · Shepard 1967, Standing 1973
DELL TECHNOLOGIESDell Data Science Foundations
The Dell Data Science Foundations certification validates your understanding of the data analytics lifecycle, from framing business drivers and exploring data with R to applying advanced analytics methods and operationalizing results. It is designed for systems engineers and technical consultants who support data science initiatives. Earning it demonstrates that you can speak the language of data science and contribute to analytics projects with confidence.
Exam formatProctored exam delivered via Pearson VUE test centers or OnVUE online proctoring
DeliveryPearson VUE
Free questions695
Content last reviewed 30 July 2026 · Up to date
The certification
What Dell Data Science Foundations proves, and what it asks of you
What this certification covers, who it is written for, and what the exam itself looks like on the day.
6domains
27objectives
176concepts
What it is
What this certification is
What it validates, who it is written for, and the experience it assumes.
About this certification
The Dell Data Science Foundations certification (D-DS-FN-23) validates your grasp of the core concepts and practices that underpin modern data analytics. It covers the role of the data scientist, the phases of the data analytics lifecycle, and the statistical and machine-learning methods used to build and evaluate models. You will also learn how to work with big data technologies like Hadoop and MapReduce, and how to communicate findings through effective data visualization.
This certification is part of the Dell Technologies Proven Professional program, which recognizes skills across cloud, infrastructure, and data-centric roles. It is a foundational credential that prepares you for more advanced data science and AI-focused certifications, and it signals to employers that you understand the end-to-end analytics workflow—from raw data to actionable insight.
Who it’s for
This certification is intended for systems engineers and technical consultants who work with or support data science and big data analytics initiatives. It is also valuable for IT professionals who need to understand how data analytics projects are structured and executed, even if they are not data scientists themselves. You are likely a good fit if you are comfortable with technical concepts, have some exposure to data or analytics tools, and want to build a foundation in data science terminology, methods, and workflows. No prior data science certification is required.
Recommended experience
Dell recommends that candidates complete the recommended training and have some familiarity with data analytics concepts, though no specific experience is mandated. Familiarity with basic statistics and data analysis concepts; Understanding of the data analytics lifecycle and common analytics methods; Exposure to tools like R, Hadoop, or SQL is helpful but not required
The syllabus
What you’ll learn
Every domain and objective Dell Technologies measures, with the weight they carry on the exam.
The official Dell Technologies exam outline · checked 30 July 2026 · See the source
Big Data, Analytics, and the Data Scientist Role
- 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
- 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
- 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
- K-means clustering
- Association rules
- Linear regression
- Logistic Regression
- Nave Bayesian classifiers
- Decision trees
- Time Series Analysis
- Text Analytics
Advanced Analytics for Big Data - Technology and Tools
- 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
- 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
On the day
The exam itself
Everything Dell Technologies publishes about sitting it, and nothing we inferred.
Prerequisites
No mandatory prerequisites — this certification has no required predecessor exam or credential.
CertificationDell Data Science Foundations
Exam formatProctored exam delivered via Pearson VUE test centers or OnVUE online proctoring
DeliveryPearson VUE
LanguagesEnglish, French, Japanese
After you pass
Where this credential goes next
The path Dell Technologies lays out, how the credential is kept, and where to book.
Step-by-step path to Dell Data Science Foundations
Credential earnedDell Data Science Foundations Certification Lifecycle status
This certification is currently active and available. Dell Technologies maintains this certification to validate current skills and industry relevance.
Exam status: ActiveMaintained by Dell Technologies
Exam registration
Register for the exam through Pearson VUE, Dell Technologies’s authorized testing partner.
Schedule your examVisit the official Dell Technologies certification page for exam policies and requirements.
View the official pageYour coach
And when you are serious, your coach Pip takes over
Your coach in the app reads what you have answered with the book closed and tells you one thing to do tonight. It will not count an answer you gave with the page open, and it will tell you when you are not ready.
See how the coach worksBefore you book
Questions people ask
How does the Data Science Foundations exam relate to other Dell data science certifications?
The Data Science Foundations certification is a foundational credential that can lead to more advanced data science and AI-focused certifications, such as the Data Science Optimize solution badge or the Model Augmentation and Data Engineering with NVIDIA solution certification.
Do I need to earn a lower-level Dell certification before taking D-DS-FN-23?
No. Dell lists no mandatory prerequisites for this exam. You can take it directly, though completing recommended training is advised.
How do I schedule, reschedule, or cancel my exam appointment?
You can schedule, reschedule, or cancel your exam through your Pearson VUE account. Cancellations made less than 24 hours in advance are subject to a same-day forfeit fee, and no-shows are charged the full exam fee.
What are the check-in and online proctoring requirements?
You must present a valid government-issued photo ID at check-in. For online proctoring via OnVUE, you must follow Pearson VUE's environment and system requirements, including a quiet, private space and a webcam.
What is the retake policy if I fail the exam?
If you do not pass on your first attempt, you must wait 7 days before retaking. After a second failed attempt, you must wait at least 14 days between each additional attempt. If you need to retake an exam you have already passed to fulfill a certification track requirement, you may do so after 3 months.
Are there any hands-on or lab components in the exam?
The exam is a proctored skills certification exam delivered through Pearson VUE test centers or OnVUE. It does not include a separate hands-on lab component; it is a knowledge-based exam.
How soon will I receive my exam results?
Exams are computer scored immediately, and you will receive a provisional score report at the test center or online. The final score is transmitted to your CertTracker account within 72 hours after statistical analysis is completed.
What job roles does this certification align with?
The target audience includes systems engineers and technical consultants who support data science and big data analytics initiatives. It is also relevant for IT professionals seeking foundational data science knowledge.
Can I recertify by passing a different, higher-level Dell exam?
Dell certifications must be renewed periodically. While specific renewal paths for this certification are not published on the exam page, earning advanced certifications may contribute to maintaining your Proven Professional status. Check the Dell Learning site for current renewal policies.
Is the exam available in my region?
The exam is available in English, French, and Japanese, and is delivered through Pearson VUE test centers and OnVUE online proctoring, which are available in many countries. Check Pearson VUE's website for regional availability.
Information freshness · Content last reviewed on 2026-07-30 Up to date
Practice free questions 695 questions, free, no account needed.
The curriculum
The whole exam, mapped and written
Every domain, every objective, and every concept Dell Technologies measures — each one written out.
6domains
27objectives
176concepts
150free pages
The domains, and what sits inside them
Every objective below is a page you can open and practise now, without an account.
The official Dell Technologies exam outline · checked 30 July 2026 · See the source
1Big Data, Analytics, and the Data Scientist Role68 questions
2Data Analytics Lifecycle120 questions
Describe the data analytics lifecycle purpose and sequence of phases3 concepts · 6 pagesOpen →Discovery - Describe details of this phase, including activities and associated roles4 concepts · 6 pagesOpen →Data preparation - Describe details of this phase, including activities and associated roles3 concepts · 4 pagesOpen →Model planning - Describe details of this phase, including activities and associated roles4 concepts · 6 pagesOpen →Model building - Describe details of this phase, including activities and associated roles3 concepts · 4 pagesOpen →
3Initial Analysis of the Data65 questions
Explain how basic R commands are used to initially explore and analyze the data7 concepts · 5 pagesOpen →Describe and provide examples of the most important statistical measures and effective visualizations of data5 concepts · 6 pagesOpen →Describe the theory, process, and analysis of results for hypothesis testing and its use in evaluating a model4 concepts · 3 pagesOpen →
4Advanced Analytics - Theory, Application, and Interpretation of Results for Eight Methods206 questions
K-means clustering8 concepts · 6 pagesOpen →Association rules4 concepts · 4 pagesOpen →Linear regression12 concepts · 7 pagesOpen →Logistic Regression8 concepts · 5 pagesOpen →Nave Bayesian classifiers9 concepts · 5 pagesOpen →Decision trees7 concepts · 5 pagesOpen →Time Series Analysis8 concepts · 6 pagesOpen →Text Analytics8 concepts · 6 pagesOpen →
5Advanced Analytics for Big Data - Technology and Tools161 questions
Describe the technological challenges posed by Big Data17 concepts · 9 pagesOpen →Describe the nature and use of MapReduce and Apache Hadoop6 concepts · 6 pagesOpen →Describe the Hadoop ecosystem and related product use cases16 concepts · 11 pagesOpen →Describe in-database analytics and SQL essentials6 concepts · 4 pagesOpen →Describe advanced SQL methods: window functions, ordered aggregates, and MADlib5 concepts · 5 pagesOpen →
6Operationalizing an Analytics Project and Data Visualization Techniques75 questions
Describe best practices for communicating findings and operationalizing an analytics project4 concepts · 5 pagesOpen →Describe best practices for building project presentations for specific audiences5 concepts · 6 pagesOpen →Describe best practices for planning and creating effective data visualizations5 concepts · 5 pagesOpen →
Not a table of contents
Every line on this map already opens
In front of every objective the practice pages are already there, free and without an account. This is one objective, opened.
Define and describe the characteristics of Big Data4 pagesOpen →
Free in front of it
18 questions on this objective, five to a page. Every range above is a real page, open now, with no account.
Reading the map is not the same as knowing the ground
The curriculum tells you what is on the exam. Proving you know it is a different job — and it is the one the closed-book run does.
Free questions
695 questions. No account, no card.
The whole bank is open. 5 questions to a page, every answer explained, and a discussion thread on each one.
695questions
150pages
27objectives
Freealways
Browse by domain
Every objective, and every page range, is a link — so you can pick up exactly where you left off.
1Big Data, Analytics, and the Data Scientist Role68 questions
2Data Analytics Lifecycle120 questions
3Initial Analysis of the Data65 questions
4Advanced Analytics - Theory, Application, and Interpretation of Results for Eight Methods206 questions
5Advanced Analytics for Big Data - Technology and Tools161 questions
6Operationalizing an Analytics Project and Data Visualization Techniques75 questions
What a practice page gives you
1Five to a page
Short enough to finish, long enough to matter.
2Every answer explained
Not only which one is right — why the others are wrong.
3A discussion on each
Ask, answer, and vote. Every question has its own thread.
A question, and the idea behind it
Every question comes from a concept
These are not trivia. Each one is written against a concept in the book, so when you get one wrong there is somewhere to go and find out why.
The visual book
See it once. Carry it into the exam.
The pages shown here come from our AI-900 book — an example of how each concept is written in plain language and, where the idea needs one, drawn as a full page you can take in at a glance.
176concepts
695free questions
Why a picture, and not another paragraph
Three reasons, and each one is a real finding rather than a slogan.
You can see it againThe whole idea at onceWhere it starts, what happens in the middle, what comes out, and the mistake to avoid.
Multimedia principle · MayerThe look-alikes sit togetherThe pairs the exam tests are drawn side by side, so the difference is seen, not told.
Dual coding · PaivioWhat one page is made of
You are never asked to read a poster here — only to see how one is built. After that, every other page is legible at a glance.
1A numbered strip on top
The idea as a sequence, followed with a finger before a word is read.
2Panels for the parts
What it is, how the machine learns it, when it is the right tool.
3The trap, framed
The distinction the exam tests, given its own box instead of buried in prose.
4One line at the bottom
The sentence to carry into the exam room.
Open book, on purpose
Answer it with the picture beside you
This is the part that teaches. The illustration and the written explanation stay where they are while you work, so a scenario stops being a memory test and becomes something you can simply look at.
It teaches · it does not count
A smartphone uses AI to unlock when the owner looks at the camera. Which AI capability is being used?
AImage classification
BFacial recognition
COptical character recognition
DObject detection
Look at the five steps. Detect finds a face, align straightens it, encode turns it into numbers, search compares it with a stored face, and decide answers one question: is this the same person? Classification would only say “a face”. Detection would only draw a box. Neither decides whose.
Then the page shuts, and it starts counting
The same questions come back with the book closed — that run is the one that counts. After it, your coach picks one thing for tonight, sized to the time you have, and brings pages back before you lose them.
Testing effect · Roediger & Karpicke 2006 · spacing effect · Cepeda et al. 2006
Official resources
Straight from Dell Technologies
Where the exam is defined, scheduled and scored.
3official links
176concepts mapped
The source
Dell Technologies’s own pages
We link to them rather than repeat them, so nothing here goes stale behind them.
Checked against the source
Every objective on the official Dell Technologies outline already has questions here
We build from the official skills outline, not from a summary of it — 27 objectives, 176 concepts written under them, and free questions against every one. When Dell Technologies changes the outline, this page changes with it.
27objectives on the outline→176concepts written→695free questions
An official page tells you what will be asked. It cannot tell you what you will get right.
That is the only question worth answering the night before, and no link answers it. You answer it by taking the questions with the book closed, and seeing what comes back.