What is the SAS regression and modeling certification?

The SAS regression and modeling certification is SAS’s advanced analytics credential for analysts who build and evaluate statistical models in SAS 9, formally the SAS Certified Statistical Business Analyst Using SAS 9: Regression and Modeling and carrying the exam code A00-240. It covers ANOVA, linear regression, logistic regression, input preparation and model assessment across 60 questions.

SAS files it under Advanced Analytics rather than under programming, and lists it among its most popular credentials. That placement is the clearest statement of what the exam is for: it assumes you can already write SAS and asks whether you can do statistics with it.

What distinguishes it from a general statistics qualification is how specific the syllabus is. It does not ask whether you understand analysis of variance in the abstract. It names the procedures, the statements and the options, down to which option of which statement performs a particular test.

Why does the exam name describe only half of it?

Add up the domains that are about fitting a model and you get 55 percent: ANOVA at 10, linear regression at 20 and logistic regression at 25. Add up the two that are not, Prepare Inputs for Predictive Model Performance at 20 and Measure Model Performance at 25, and you get 45 percent.

That is a much more even split than the title suggests, and it changes who the exam is hard for. An analyst who fits models daily but never formally assesses them is missing nearly half the paper. An analyst who does the full modelling cycle, including honest assessment, is already most of the way there.

The two non-fitting domains are also the ones least likely to have been learned informally. Fitting a model is what a course teaches. Deciding which candidate inputs belong in it, and then measuring whether the fitted model actually performs, tends to be learned on the job or not at all.

The quickest way to find out which side you are on is to work a mixed set of exam-style items and notice which ones slow you down. The sample sets on the money site’s A00-240 practice exam mix the five domains in roughly their published proportions, which makes the gap visible in an hour rather than on results day.

How is A00-240 delivered and scored?

A00-240 is 60 questions in 110 minutes with a 68 percent pass mark, priced at $180 USD and delivered through Pearson VUE. SAS publishes the $180 figure on its own certification page, and the money site’s syllabus supplies the question count, duration and pass mark that SAS does not publish anywhere.

Sixty questions in 110 minutes gives 110 seconds each, which is generous for a multiple-choice paper and is clearly deliberate. Several objectives ask you to interpret output rather than recall a fact, and reading a table of parameter estimates or a diffogram properly takes longer than answering a definition.

Sixty eight percent of 60 questions means 41 correct answers and a margin of 19. That is a middling allowance, and the even domain spread means it cannot absorb a whole missing domain: the smallest domain alone is worth six questions and the largest fifteen.

SAS keeps its detail thin on the web. Its certification page names the credential and the price and stops, with no per-credential page behind it, which is why the published domain weightings come from the money site.

Where do the marks sit across the five domains?

Logistic Regression and Measure Model Performance tie as the largest domains at 25 percent each, Linear Regression and Prepare Inputs tie at 20 percent each, and ANOVA is smallest at 10 percent. On a 60 question paper that is 15, 15, 12, 12 and 6 questions respectively.

Two things follow from that shape. The first is that logistic regression outweighs linear regression, which surprises people who assume the simpler technique carries more. The second is that the two assessment domains together outweigh either regression domain individually.

There is no domain small enough to ignore. Even ANOVA at 10 percent is worth six questions against a 19 question margin, so writing it off consumes nearly a third of the allowance before the paper starts.

Why is ANOVA mostly about assumptions?

The smallest domain is also the most conceptually front-loaded. Before it reaches any group comparison, the syllabus asks about the central limit theorem, the distribution of continuous variables through histograms, box-whisker plots and Q-Q plots, the effect of skewness, the null and alternative hypotheses, Type I and Type II error, statistical power, and how sample size affects both p-value and power.

Only after that does it get to the procedures. The objectives name PROC GLM with its CLASS, MODEL, MEANS and OUTPUT statements, PROC TTEST for comparing means, the HOVTEST option of the MEANS statement for assessing equal response variance, and PROC UNIVARIATE for examining residuals.

The post hoc objectives are more specific still: LSMEANS with the PDIFF option for pairwise comparisons, the ADJUST option using TUKEY and DUNNETT, and interpreting diffograms and control plots to evaluate those comparisons. Knowing that Tukey compares every pair while Dunnett compares against a control is exactly the kind of distinction a question can turn on.

The domain closes with interactions, where PROC PLM and the SLICE= option appear alongside Type I and Type III sums of squares. Six questions is not many for that much named machinery, which is why this domain rewards a focused sweep rather than deep study.

Linear and logistic regression together are 45 percent

The two regression domains hold 27 of the 60 questions between them, and they are weighted the way the working world weights them rather than the way a textbook orders them. Logistic gets 25 percent to linear’s 20, because a binary outcome is what most business modelling actually predicts.

Linear regression

The linear domain is built around fitting multiple models with PROC REG and PROC GLM, and then around everything that follows a fit: which predictors earned their place, whether the residuals behave, and whether the model generalises. It is the domain where an analyst’s informal habits are most likely to be tested against formal practice.

Logistic regression

Logistic carries the extra five points and deserves the extra attention. The recommended SAS course list names Predictive Modeling Using Logistic Regression specifically, which is a strong signal about the depth expected. SAS’s documentation hub is the place to read the procedure detail, since the statistical procedure pages themselves are the reference the objectives are written against.

The conceptual jump candidates underestimate is interpretation. A linear coefficient is a change in the outcome. A logistic coefficient is a change in log odds, and converting that into something a business audience can act on is a separate skill from fitting the model.

What does preparing inputs actually involve?

Prepare Inputs for Predictive Model Performance is 20 percent, about 12 questions, and it covers the work that happens between having data and having a model worth fitting. It is the domain most often skipped in self-study because it feels like preparation rather than technique.

In practice it is technique. Deciding how to handle missing values, how to treat outliers and extreme values, how to represent categorical variables, and which candidate inputs to carry forward are all decisions with consequences that show up much later, in the assessment domain, as a model that fits the training data and nothing else.

That connection is why the two domains sit adjacent on the syllabus and together carry 45 percent. Input preparation is where overfitting is created, and model measurement is where it is discovered. Reading the two as one continuous subject rather than as two separate ones is the most useful reframing available for this exam.

Anyone whose SAS background is programming rather than statistics will find this the least familiar of the five domains. The SAS programming fundamentals exam covers the language rather than the modelling discipline, so a strong programming credential does not close this particular gap.

What does measuring model performance involve?

At 25 percent, about 15 questions, Measure Model Performance is tied with logistic regression as the largest domain on the paper. It asks whether a fitted model performs on data it has not seen, which is a different question from whether it fits the data it was built on.

This is the domain where honest practice and exam practice align most closely. Splitting data for validation, comparing candidate models on a common basis, and reading the curves and statistics that describe discrimination are the things a working analyst does before presenting a model to anyone.

The vocabulary is not SAS-specific, which makes independent material genuinely useful here. Practical model evaluation walkthroughs such as Kaggle’s machine learning course cover the same assessment logic from a different toolchain, and understanding the concept outside SAS makes the SAS output easier to read rather than harder.

For candidates weighing the exam against the effort, the assessment skills are also the most portable thing it certifies. Fitting procedures are tied to SAS; judging whether a model is any good is not, which is part of why analyst roles that name this credential tend to pay around the broader data analyst band reported by sources such as PayScale’s data analyst data rather than a SAS-specific premium.

How should you prepare for A00-240?

Preparation for A00-240 should treat the exam as two halves rather than five domains: the fitting half at 55 percent and the assessment half at 45 percent. SAS’s own recommended route is Statistics 1 for the ANOVA and regression material, followed by Predictive Modeling Using Logistic Regression for the heavier logistic content.

1. Work out which half you are weaker on, because an analyst who fits models daily and an analyst who evaluates them are missing opposite parts of this paper.
2. Sweep the ANOVA assumptions material first, covering the central limit theorem, the distribution plots, hypothesis framing, error types and power, since six questions rest on concepts you can settle quickly.
3. Run PROC GLM and PROC TTEST against the same data and compare what each tells you, then use LSMEANS with PDIFF and both the TUKEY and DUNNETT adjustments so the difference between them is experiential.
4. Fit multiple linear models with PROC REG and PROC GLM, then examine the residuals with PROC UNIVARIATE rather than accepting the fit statistics at face value.
5. Give logistic regression the largest single block of study time, and practise converting coefficients into something interpretable rather than stopping at the model output.
6. Build a candidate input set deliberately, making explicit decisions about missing values, extreme values and categorical representation, then record why you made each one.
7. Hold data back and measure the model on it, comparing at least two candidate models on the same basis so that assessment becomes a comparison rather than a single verdict.
8. Finish with timed sets of 60 questions in 110 minutes, using the generous clock to read output carefully rather than to second-guess answers you already know.

Six to ten weeks is realistic for a working SAS analyst, and the range depends almost entirely on how much formal statistics sits behind the day job. Someone who already validates models properly can move quickly; someone who has only ever fitted them should plan for the longer end.

On whether the credential earns its keep, the honest answer depends on the role you are aiming at rather than the exam itself, and this credential and your career is worth thinking through before you book rather than after you pass.

Frequently Asked Questions

How many questions are on the A00-240 exam?

Sixty questions in 110 minutes, which is 110 seconds each. The generous pace reflects how many objectives ask you to interpret output rather than recall a definition.

What is the passing score for the SAS regression and modeling certification?

Sixty eight percent, meaning 41 correct answers out of 60 and a margin of 19. That figure comes from the money site’s syllabus page, since SAS publishes no passing score on its own site.

How much does A00-240 cost?

$180 USD, delivered through Pearson VUE. SAS publishes that price on its own certification page, so it is the one numeric field the vendor and the money site both state.

Which domain carries the most marks?

Two tie at 25 percent each: Logistic Regression and Measure Model Performance. Linear Regression and Prepare Inputs follow at 20 percent each, and ANOVA is smallest at 10 percent.

Is this a statistics exam or a SAS exam?

Both, and the syllabus is specific about the SAS half. It names procedures, statements and options directly, including PROC GLM, PROC TTEST, PROC UNIVARIATE, PROC PLM, LSMEANS with PDIFF and ADJUST, and the HOVTEST option.

Does the exam cover logistic regression more than linear?

Yes, by five points. Logistic carries 25 percent against linear’s 20, which reflects how often business modelling predicts a binary outcome rather than a continuous one.

Is there a prerequisite certification?

None is published. SAS lists no prerequisite for this credential, although the exam assumes you can already write SAS code rather than teaching the language.

What is the difference between Tukey and Dunnett adjustments?

Tukey compares every pair of group means against each other; Dunnett compares each group against a single control. The syllabus names both explicitly under the post hoc objectives.

Does a SAS programming credential prepare you for this one?

Only partly. A programming credential covers the language, while this exam covers the modelling discipline. The input preparation and model measurement domains, worth 45 percent between them, are not language topics at all.

Is A00-240 still current?

Yes. SAS lists the credential in two places on its own certification page, under Most Popular Credentials and under Advanced Analytics, with no retirement notice. SAS also runs a separate Viya credential line, which is a different platform rather than a replacement for this exam.

Conclusion

A00-240 is an even paper wearing an uneven name: 60 questions, 110 minutes, $180, a 68 percent bar, and five domains at 25, 25, 20, 20 and 10. Fitting models accounts for 55 percent of it and everything around the fit accounts for 45.

Read the two assessment domains as one continuous subject, because input preparation is where a model goes wrong and model measurement is where you find out. Give logistic regression the largest block, sweep the ANOVA assumptions quickly rather than deeply, and use the generous clock in the exam to read the output properly instead of rushing a question you could have answered from the table in front of you.