- What the Code Actually Identifies
- What the Credential Validates
- The Six Exam Domains and Their Weights
- R, Hadoop/MapReduce, and SQL/MADlib: The Named Toolset
- Exam Format, Delivery, and Retakes
- Registration and Voucher Mechanics
- Official Preparation Path
- Who Should Take It and Where It Leads
- Sequencing Your Study by Domain
- Frequently Asked Questions
- D-DS-FN-23 is a Dell Technologies Proven Professional exam; Dell now titles the code "Dell Data Science Foundations."
- Eight-method advanced analytics is the largest domain at 40%, followed by Big Data technology and tools at 22%.
- The exam runs 90 minutes in English, French, or Japanese, delivered through Pearson VUE test centers or OnVUE.
- Dell's recommended prep is the Data Science and Big Data Analytics v2 on-demand course (ES732OCMDSBDA) and lab (ES742OCMDSALN).
What the Code Actually Identifies
D-DS-FN-23 is the exam code for the Dell Technologies Proven Professional certification titled Dell Data Scientist and Big Data Analytics Foundations 2023. Dell's current catalog lists the same code under the shorter title Dell Data Science Foundations, but the long-form 2023 name remains the one most candidates search for, and both names point to the same exam. If you want a quick orientation on the naming, our pages on the D-DS-FN-23 meaning and what D-DS-FN-23 stands for break the code apart piece by piece.
The "23" refers to the 2023 exam generation. According to Dell's transformation documentation, this exam replaced the earlier DEA-7TT2 code, with the migration effective February 3, 2024. If you encounter older study material referencing DEA-7TT2, it describes the predecessor rather than this exam, so treat it as background and confirm every objective against the current D-DS-FN-23 exam description.
What the Credential Validates
Dell positions this exam as a foundations-level validation of data science and big data analytics knowledge. The requirement is straightforward: candidates need sufficient practical knowledge and skills, gained through experience, recommended training, or both, and then pass D-DS-FN-23. There is no mandatory prerequisite certification listed for this exact code in the sources we reviewed, which is covered further in our guide to D-DS-FN-23 requirements.
What the exam actually tests is a full-stack understanding of the analytics workflow: how a data scientist frames a problem, moves through the Data Analytics Lifecycle, explores and conditions data, chooses among a defined set of analytic methods, runs those methods on big data platforms, and finally operationalizes and communicates the results. It is as much about reasoning (which method fits this business question, and how do I interpret the output?) as about recall.
The Six Exam Domains and Their Weights
Dell publishes six objective areas with approximate question-distribution weights. The distribution is heavily skewed toward one domain, which should shape how you spend your time. For a deeper walkthrough of each area, see the complete guide to all 6 content areas.
| Domain | Approximate Weight |
|---|---|
| Domain 1: Big Data, Analytics, and the Data Scientist Role | 5% |
| Domain 2: Data Analytics Lifecycle | 8% |
| Domain 3: Initial Analysis of the Data | 15% |
| Domain 4: Advanced Analytics - Theory, Application, and Interpretation of Results for Eight Methods | 40% |
| Domain 5: Advanced Analytics for Big Data - Technology and Tools | 22% |
| Domain 6: Operationalizing an Analytics Project and Data Visualization Techniques | 10% |
Domain 1: Big Data, Analytics, and the Data Scientist Role (5%)
The smallest domain sets context: what makes data "big," how analytics differs from traditional business intelligence, and what a data scientist does within a team.
- Characteristics of big data and the drivers behind its growth
- The data scientist's responsibilities relative to other roles in an analytics project
- Why advanced analytics extends beyond reporting and dashboards
Domain 2: Data Analytics Lifecycle (8%)
This domain covers the phased lifecycle that structures an analytics project from discovery through delivery. Dell's complete exam PDF explicitly includes model building within this lifecycle coverage, so do not skip that phase even if other summaries omit it.
- The purpose and outputs of each lifecycle phase
- Discovery work such as framing the problem and forming initial hypotheses
- Data preparation versus model planning versus model building
Domain 3: Initial Analysis of the Data (15%)
Before any advanced method is applied, data must be explored and understood. This domain is where basic statistics and R-based exploration live.
- Descriptive statistics, distributions, and exploratory visualization
- Hypothesis testing and interpreting results
- Recognizing data quality problems before modeling
Domain 4: Advanced Analytics - Eight Methods (40%)
The heart of the exam. You must understand the theory behind eight analytic methods, when each is appropriate, how to apply it, and how to interpret its output. Because this domain carries roughly four of every ten questions, weakness here is difficult to offset elsewhere.
- Matching a business question to the right analytic method
- Reading model output and explaining what it means in plain terms
- Understanding assumptions, limitations, and common pitfalls of each method
Domain 5: Advanced Analytics for Big Data - Technology and Tools (22%)
The second-largest domain shifts from methods to platforms: how analytics scales when data outgrows a single machine.
- Hadoop and the MapReduce processing model
- In-database analytics with SQL and MADlib
- Choosing the right technology for a given data volume and workload
Domain 6: Operationalizing an Analytics Project and Data Visualization Techniques (10%)
The closing domain covers getting results into the hands of decision-makers and communicating them effectively.
- Deliverables and what different audiences need to see
- Moving a model from a project into ongoing production use
- Choosing visualization techniques that support the message
Key Takeaway
Domains 4 and 5 together account for roughly 62% of the exam. A candidate who is strong on methods and big data tooling but light on the role-and-context material in Domain 1 is in a far better position than the reverse.
R, Hadoop/MapReduce, and SQL/MADlib: The Named Toolset
One distinguishing feature of this exam is that its scope explicitly names its technologies. Dell's exact scope includes R, Hadoop/MapReduce, and SQL/MADlib. That makes the exam more concrete than a purely conceptual analytics test, though it is still a foundations exam rather than a coding assessment.
R for exploration and modeling
R appears primarily in the context of initial data analysis and in demonstrating the advanced methods. Expect to recognize what a piece of R output means, such as a summary statistic, a model coefficient table, or a diagnostic plot, and to reason about what it implies for the analysis.
Hadoop and MapReduce for scale
For the big data portion, you should understand how MapReduce divides work across a cluster, what the map and reduce steps accomplish conceptually, and where this model fits and does not fit. The questions are about understanding the processing paradigm, not writing production cluster code.
SQL and MADlib for in-database analytics
MADlib brings analytic functions to the database, so analysis runs where the data lives instead of requiring a large data movement. Be comfortable with why in-database analytics is attractive and how SQL-driven workflows differ from exporting data into a separate statistical environment.
Exam Format, Delivery, and Retakes
Dell publishes a 90-minute exam duration and offers the exam in English, French, and Japanese. Delivery is through Pearson VUE, either at a proctored test center or via OnVUE online proctoring.
Dell's retrieved exact-code sources do not publish the question count, the passing score, or a pass rate. Anything quoting precise figures for those should be treated with caution unless it traces back to Dell. We cover what is and is not known on our pages about the passing score and the pass rate data.
What to expect on test day
- Results are computer-scored and treated as provisional pending Dell review.
- OnVUE candidates must pass a system test beforehand, present a matching government photo ID, and test in a private room without books or notes.
- The preparation lab recommended by Dell does not establish a separately timed practical exam; the credential is earned through this single exam.
Retake policy
If you do not pass, you wait 7 days before your first retake. After any later failure, the wait extends to 14 days. Plan accordingly if a deadline is attached to your attempt, and treat a first-attempt failure as a diagnostic to guide a focused second round rather than a reason to start over.
Registration and Voucher Mechanics
Candidates register through Pearson VUE's Dell program. Pearson's U.S. voucher store lists the general Dell certification voucher at USD 230, and the voucher expires 12 months after purchase. That expiry is a purchase-validity window for scheduling your exam; it is separate from the validity of any credential you earn.
Credential status is managed in Dell's CertTracker, and recognition follows Dell's current requirements, including revised exams when required. For the complete financial picture beyond the voucher, see our certification cost breakdown, and for scheduling logistics see the guide to exam dates and scheduling.
Official Preparation Path
Dell's recommended preparation centers on two resources: the Data Science and Big Data Analytics v2 on-demand course (ES732OCMDSBDA) and its companion lab (ES742OCMDSALN). The accompanying textbook is optional. Because the exam draws on both conceptual and tool-oriented material, the lab is valuable for making R, MapReduce, and MADlib concepts tangible even though it is not itself a separately timed test.
Pair the official course with focused practice questions that mirror the scenario-driven style of the exam: a business problem, a dataset description, and a decision about method or tooling. Our D-DS-FN-23 training overview and the full study guide go deeper on building a plan, and you can test your readiness with the D-DS-FN-23 practice tests.
Who Should Take It and Where It Leads
This credential suits professionals who work with or around analytics teams and want a vendor-recognized baseline in data science and big data fundamentals. Typical candidates include:
- Aspiring data scientists and junior analysts building a verified foundation
- Business and data analysts moving from reporting toward predictive and advanced methods
- Database and infrastructure professionals supporting Hadoop or in-database analytics environments
- IT and presales staff in Dell-aligned organizations who need credible fluency in analytics conversations
Employers that value Dell Technologies expertise, including Dell partners, systems integrators, and enterprises running Dell data infrastructure, are the most natural audience. We avoid quoting salary numbers here because none are published by Dell for this credential; our salary guide and jobs overview discuss the role landscape qualitatively, and the ROI analysis helps you weigh the investment against your own goals. Candidates sizing up the challenge can also read how hard the exam is in our difficulty guide.
Sequencing Your Study by Domain
Rather than generic scheduling advice, let the blueprint weights drive your order. A sensible arc puts foundations early, the heaviest domain in the middle with the most repetition, and tooling and delivery afterward:
Context and Lifecycle (Domains 1-2)
- Learn the data scientist role and the lifecycle phases, including model building
- These domains total 13%, so move quickly but build the vocabulary used everywhere else
Initial Data Analysis (Domain 3)
- Work through exploration, descriptive statistics, and hypothesis testing in R
- Practice reading output rather than writing code
The Eight Methods (Domain 4)
- Give this domain the most time since it carries 40%
- For each method, practice choosing it, interpreting output, and naming its limitations
Big Data Tools and Delivery (Domains 5-6)
- Cover Hadoop/MapReduce and SQL/MADlib, then operationalization and visualization
- Finish with timed mixed-domain practice sets against the 90-minute limit
Adjust the pacing to your background. A working statistician may compress Domain 3, while a database administrator may need extra time on the analytic methods.
Frequently Asked Questions
It is the exam code for Dell Data Scientist and Big Data Analytics Foundations 2023, a Dell Technologies Proven Professional exam that Dell's catalog now titles Dell Data Science Foundations. See our explainers on what it stands for and what the certification is.
The published duration is 90 minutes. The exam is offered in English, French, and Japanese, delivered through Pearson VUE at a test center or via OnVUE.
Advanced analytics covering eight methods is the largest at 40%, followed by Big Data technology and tools at 22%. Together they make up well over half of the exam.
You must wait 7 days before retaking after a first failure, and 14 days after any later failure. Use the waiting period to revisit the domains where you were weakest.
Not in the exact-code sources we reviewed. The question count, passing score, and pass rate are unpublished there, so be skeptical of any site presenting exact figures without citing Dell.