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Exam Information
Official specifications published by Dell
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Skills this practice test covers
- Describe the characteristics of Big Data and the skills a data scientist needs.
- Place an activity in the right phase of the Data Analytics Lifecycle.
- Explore a dataset with basic R commands, summary statistics and charts.
- Choose among the eight analytics methods and interpret the output of each.
- Explain how MapReduce, Hadoop and in-database SQL handle very large data.
- Plan a project hand-over and present the findings to different audiences.
Explore related topics
D-DS-FN-23 exam content
6 domains · 21 topics · 158 of 288 questions sorted by domain
Big Data, Analytics, and the Data Scientist Role5%18 questions
The smallest domain sets the scene. Candidates describe what makes data big, why businesses invest in analytics, and what a data scientist brings to a team.
Skills tested
- Describe Big Data by its volume, velocity and variety.
- Explain the business drivers for Big Data analytics and data science.
- Describe the Data Scientist role and the skills that go with it.
Questions by topic
- Characteristics of Big Data6
- Data Scientist role and related skills7
- Business drivers for Big Data analytics5
Data Analytics Lifecycle8%36 questions
This domain walks through the phases of an analytics project. Candidates describe each phase, its activities and the people involved, and explain why teams loop back to earlier phases.
Skills tested
- Put the lifecycle phases in order and give the purpose of each one.
- Describe the Discovery phase: learning the business domain, finding the stakeholders and framing the goal.
- Explain the work done in data preparation, model planning and model building, and who does it.
Questions by topic
- Data preparation phase activities6
- Discovery phase activities6
- Model building phase activities5
- Iterative nature of the lifecycle6
- Model planning phase activities5
- Data preparation6
- Model building2
Initial Analysis of the Data15%41 questions
Before any model is built, the data must be explored. Candidates use basic R commands, read statistical measures and charts, and use hypothesis testing to evaluate a model.
Skills tested
- Use basic R commands to load, summarize and explore a dataset.
- Choose a statistical measure or chart, such as a histogram or a boxplot, that shows the data clearly.
- Set up a hypothesis test and read its result.
Questions by topic
- Basic R commands for data exploration12
- Hypothesis testing11
- Effective visualizations of data9
- Statistical measures8
- Data preparation1
Advanced Analytics - Theory, Application, and Interpretation40%
The largest domain covers eight analytics methods. For each one, candidates explain how it works, when to apply it and how to read its results.
Skills tested
- Group records with K-means clustering and choose a sensible number of clusters.
- Find association rules and judge them by support, confidence and lift.
- Fit linear and logistic regression models and interpret their coefficients.
- Classify records with naïve Bayesian classifiers and decision trees, and compare the two.
- Forecast with time series analysis, and score words for text analytics with TF-IDF.
Advanced Analytics for Big Data - Technology and Tools22%63 questions
This domain covers tools for data too large for one machine. Candidates explain MapReduce and Apache Hadoop, match tools in the Hadoop ecosystem to their uses, and run analytics inside the database with SQL.
Skills tested
- Explain how MapReduce splits a job into map and reduce steps across a cluster.
- Match Hadoop ecosystem tools such as Hive, Pig and HBase to their use cases.
- Describe in-database analytics and write SQL with window functions and ordered aggregates.
- Explain what MADlib adds to a SQL database for statistical work.
Questions by topic
- Hadoop ecosystem and related product use cases22
- MapReduce and Apache Hadoop12
- Advanced SQL methods11
- In-database analytics6
- In-database analytics and SQL essentials6
- Technological challenges posed by Big Data6
Operationalizing and Data Visualization10%
The last domain turns results into action. Candidates plan how to put a model into use, build presentations for different audiences, and design clear charts.
Skills tested
- Plan how to operationalize an analytics project and hand it over to the business.
- Build one presentation for project sponsors and another for technical staff.
- Pick a chart type that fits the data, and remove clutter from it.
Requirements
- No formal prerequisites. Dell expects the skills to come from hands-on experience or its recommended training.
- Recommended: a basic understanding of statistics.
- Recommended: familiarity with programming concepts and some data analysis experience.
About the D-DS-FN-23 exam
D-DS-FN-23 is Dell's foundations exam in data science. It checks that a candidate can follow an analytics project from the business question to the final presentation. Passing it earns Dell's Data Science Foundations certification in the Proven Professional program. The largest part asks which method fits a problem, such as clustering or regression, and how to read its results. The content is vendor-neutral: it is built on R, statistics, Hadoop and SQL, not on Dell products.
The PlanetCert practice test explains each answer through the method behind it. For example, an explanation shows why K-means needs the number of clusters chosen in advance. Study Mode suits the eight methods, where each output deserves a slow read. Flashcard Mode helps fix the Hadoop tools and SQL terms in memory. Brainy AI Assistant can walk through a regression or clustering result one step at a time.
How to earn the certification
- Earns
- Dell Technologies Proven Professional Data Science Foundations CertifiedFoundations
How to prepare for D-DS-FN-23
- 1
Install R and explore a dataset
Install R, load a small public dataset, and describe it with summary statistics, a histogram and a boxplot. These are the basic R commands the initial analysis domain expects.
- 2
Learn the lifecycle phases in order
Write the lifecycle phases from memory, with one activity and one role for each. Then practise naming the phase that a described activity belongs to.
- 3
Run each of the eight methods in R
The advanced analytics domain is the largest, so give it the most time. Run each method on sample data and explain its output in plain words: clusters, rules, coefficients, class labels, a forecast and word scores.
- 4
Sketch MapReduce on paper
Draw how a word count runs as map and reduce steps across a cluster, then note where Hive, Pig and HBase fit. Finish by writing one SQL query with a window function.
- 5
Close with the practice test
Work through the D-DS-FN-23 practice test domain by domain in Study Mode, then sit a Timed Mode run. Reread the explanation for any method whose output was misread.
Start a timed D-DS-FN-23run
Dell recommends its Data Science and Big Data Analytics v2 On-Demand Course and On-Demand Lab. The textbook Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data supports the course and is optional.
Who D-DS-FN-23 is for
- Data analysts who want to move into data science work.
- Graduates and career changers who need a structured start in analytics.
- IT staff who run Hadoop clusters or analytics databases and want to understand the analysis.
- Business analysts who brief data science teams and read their results.
Typical job titles: Data analyst · Junior data scientist · Business intelligence analyst · Data engineer
D-DS-FN-23 glossary
16 terms from the exam objectives and the vendor's official material, each defined in a sentence.
- Apache Hadoop
- Open-source software that stores very large data sets across a cluster of machines and processes them in parallel.
- Association rules
- If-then patterns found in transaction data, such as shoppers who buy bread also buying butter.
- Big Data
- Data too large, too fast or too varied for ordinary databases and tools to handle well.
- Data Analytics Lifecycle
- The phases an analytics project moves through: discovery, data preparation, model planning, model building, communicating results and operationalizing.
- Decision trees
- Models that classify a record by asking a series of questions about its variables, one branch at a time.
- Hypothesis testing
- A method for deciding whether an effect seen in the data is real or could be due to chance.
- In-database analytics
- Running the analysis inside the database where the data is stored, instead of copying the data out first.
- K-means clustering
- A method that splits records into K groups, placing each record in the group whose center is nearest.
- Linear regression
- A method that predicts a number, such as a price, from input variables by fitting a straight-line relationship.
- Logistic Regression
- A method that predicts the probability of a yes-or-no outcome, such as whether a customer will leave.
- MADlib
- An open-source library of statistical and machine learning functions that run inside a SQL database.
- MapReduce
- A programming model that splits a big job into map tasks that run in parallel and reduce tasks that combine their results.
- Naïve Bayesian classifiers
- Classifiers that apply Bayes' theorem and assume the input variables are independent of each other.
- TF-IDF
- A score for how important a word is to one document, weighed against how common it is across all documents.
- Time Series Analysis
- Methods for values measured over time, such as monthly sales, that find trends and forecast future values.
- Window functions
- SQL functions that calculate over a set of rows related to the current row, such as a running total.
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