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100+ Free SAS Statistical Business Analyst Practice Questions

Pass your SAS Certified Specialist: Statistical Business Analyst Using SAS 9 (A00-240) exam on the first try — instant access, no signup required.

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Why must input preparation steps (WOE coding, binning, mean imputation) be 'frozen' from training and applied to new data without recomputation?

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2026 Statistics

Key Facts: SAS Statistical Business Analyst Exam

60

Scored Questions

SAS official credential page

110 min

Exam Duration

SAS official credential page

68%

Passing Score

SAS official credential page

$180

Exam Fee

SAS global exam pricing

A00-240

Exam Code

SAS certification registry

5

Domain Areas

SAS exam content guide

A00-240 is a 60-question, 110-minute Pearson VUE exam at $180 USD requiring 68% to pass. Five domains: ANOVA (10%), Linear Regression (20%), Logistic Regression (25%), Preparing Inputs for Predictive Models (20%), Measuring Model Performance (25%). The exam targets analysts who use PROC GLM, PROC REG, PROC GLMSELECT, and PROC LOGISTIC to build, validate, and deploy scorecards and predictive models on SAS 9.

Sample SAS Statistical Business Analyst Practice Questions

Try these sample questions to test your SAS Statistical Business Analyst exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1You run PROC GLM with a one-way ANOVA on four treatment groups and obtain an overall F test p-value of 0.0021. What does this result allow you to conclude?
A.All four group means are different from each other.
B.At least one group mean differs from the others, but pairwise differences require further analysis.
C.The first and last group means are different.
D.Group variances are unequal.
Explanation: A significant overall F test in PROC GLM only indicates that at least one population mean differs. Pairwise comparisons (e.g., LSMEANS with PDIFF or Tukey adjustment) are required to identify which means differ. The F test does not assess variance equality.
2Which PROC GLM statement produces adjusted treatment means that account for an unbalanced design and a continuous covariate?
A.MEANS treatment;
B.LSMEANS treatment / PDIFF ADJUST=TUKEY;
C.OUTPUT OUT=adj P=PRED;
D.ESTIMATE 'mean' treatment 1;
Explanation: LSMEANS computes least-squares (adjusted) means, which are the correct estimates when the design is unbalanced or when covariates are present. PDIFF requests pairwise differences, and ADJUST=TUKEY applies an experiment-wise correction. The MEANS statement produces unadjusted arithmetic means.
3Which assumption is NOT required for a standard one-way ANOVA F test?
A.Independent observations within and between groups.
B.Approximately normal residuals.
C.Equal sample sizes across groups.
D.Homogeneous variance across groups.
Explanation: ANOVA requires independence, normality of residuals, and equal variances (homoscedasticity). It does not require equal sample sizes; balanced designs are convenient and more robust to assumption violations, but unbalanced designs are valid.
4In PROC GLM, which statement tests a specific linear combination of treatment effects, such as comparing the average of groups A and B against group C?
A.MEANS treatment / TUKEY;
B.LSMEANS treatment;
C.CONTRAST 'A&B vs C' treatment 0.5 0.5 -1;
D.OUTPUT OUT=resid R=residual;
Explanation: The CONTRAST statement tests user-defined linear combinations of model parameters. Here the coefficients (0.5, 0.5, -1) compare the average of treatments A and B against C. ESTIMATE produces a numeric estimate, while LSMEANS provides adjusted means.
5A two-way ANOVA in PROC GLM shows a significant interaction term between TREATMENT and REGION. How should the main effects be interpreted?
A.The main effects are still the primary focus and the interaction is ignored.
B.Main effects should be interpreted within levels of the interacting variable using LSMEANS / SLICE.
C.Drop the interaction term to simplify interpretation.
D.Interaction implies multicollinearity and the model must be refit.
Explanation: When an interaction is significant, main effects can be misleading. Use LSMEANS treatment*region / SLICE=region to test the effect of treatment within each region. Removing a significant interaction biases inference.
6Which post-hoc adjustment in LSMEANS controls the family-wise error rate when performing all pairwise comparisons?
A.ADJUST=NONE
B.ADJUST=TUKEY
C.ADJUST=DUNNETT
D.ADJUST=SIMULATE(SEED=123) NSAMP=1
Explanation: Tukey's HSD adjustment controls the family-wise (experiment-wise) error rate for all pairwise comparisons. DUNNETT compares each treatment to a control (not all pairs). NONE provides no correction. SIMULATE with NSAMP=1 is invalid for multiplicity control.
7In PROC GLM output, what does the value of R-Square represent?
A.The proportion of variation in the response explained by the model.
B.The probability that the null hypothesis is true.
C.The variance of the residuals.
D.The correlation between predicted values and residuals.
Explanation: R-Square equals SSModel/SSTotal, the proportion of total variation in the response variable explained by the model. It is not a p-value or a variance estimate. By construction, predicted values and residuals are uncorrelated in OLS, with correlation 0.
8Which PROC and statement combination is most appropriate for a balanced two-factor ANOVA with no covariates?
A.PROC LOGISTIC; MODEL y = a b a*b;
B.PROC ANOVA; CLASS a b; MODEL y = a b a*b;
C.PROC REG; MODEL y = a b;
D.PROC GLMSELECT; MODEL y = a b;
Explanation: PROC ANOVA is designed for balanced designs and is computationally efficient for that case. It requires a CLASS statement listing the categorical factors. PROC GLM also works but is broader. PROC REG and PROC LOGISTIC are not appropriate for ANOVA with categorical factors directly.
9An analyst sees that residuals from a one-way ANOVA exhibit clear funneling (variance increases with the mean). Which remedy is most appropriate before re-running PROC GLM?
A.Add more covariates to the model.
B.Apply a variance-stabilizing transformation, such as log(y) or sqrt(y).
C.Drop the lowest 5% of residuals.
D.Switch from PROC GLM to PROC ANOVA.
Explanation: Funnel-shaped residuals indicate non-constant variance (heteroscedasticity). Variance-stabilizing transformations such as log or square root often correct this. Adding covariates does not fix variance issues, deleting data biases results, and PROC ANOVA has the same assumptions.
10In a randomized complete block design analyzed with PROC GLM, what is the primary purpose of including a BLOCK term in the MODEL statement?
A.To increase the residual degrees of freedom.
B.To remove between-block variability from the error term, increasing power for the treatment test.
C.To make the design unbalanced and harder to analyze.
D.To estimate the interaction between blocks and treatments.
Explanation: Blocking partitions out a known source of variation (between-block differences), shrinking the residual MSE used in the F test for treatment. This increases the power to detect true treatment effects. Blocking generally reduces residual df slightly but improves precision.

About the SAS Statistical Business Analyst Exam

The SAS Certified Specialist: Statistical Business Analyst Using SAS 9 (A00-240) credential validates the ability to use SAS/STAT software for predictive modeling, including ANOVA with PROC GLM, linear regression with PROC REG and PROC GLMSELECT, logistic regression with PROC LOGISTIC, predictive-model input preparation (WOE, IV, partitioning, oversampling), and honest model performance assessment with AUC, KS, lift, gains, and calibration.

Assessment

60 multiple-choice and short-answer questions, plus up to 5 unscored pretest items

Time Limit

110 minutes

Passing Score

68%

Exam Fee

$180 USD (SAS / Pearson VUE)

SAS Statistical Business Analyst Exam Content Outline

10%

ANOVA

PROC GLM one-way and factorial ANOVA, F tests, CONTRAST/ESTIMATE, LSMEANS with PDIFF and ADJUST=TUKEY/DUNNETT, blocking, residual diagnostics, and assumption checking.

20%

Linear Regression

PROC REG and PROC GLMSELECT for multiple linear regression: FORWARD/BACKWARD/STEPWISE/LAR/LASSO selection, AIC/SBC/CV-based stopping, R-square vs adjusted R-square, MSE, PARTITION statements, and regression diagnostics (Cook's D, leverage, DFBETAS, VIF).

25%

Logistic Regression

PROC LOGISTIC binary and ordinal models, odds ratios, -2 Log L, AIC, SC, score chi-square, c-statistic, Hosmer-Lemeshow, FIRTH for separation, ROC and ROCCONTRAST, classification tables, OUTPUT/STORE/CODE for scoring, and PROC HPLOGISTIC.

20%

Preparing Inputs for Predictive Models

Train/validation/test partitioning with PROC SURVEYSELECT, supervised and unsupervised binning (PROC HPBIN, PROC RANK), Weight of Evidence (WOE), Information Value (IV), missing-value handling, transformations, oversampling for rare events, and prior probability adjustment.

25%

Measuring Model Performance

AUC/c-statistic, KS, lift and gains, classification matrix, calibration (Hosmer-Lemeshow and calibration plots), decision-matrix profit, scorecard scaling, score code generation (CODE statement, PROC PLM RESTORE), and Population Stability Index (PSI).

How to Pass the SAS Statistical Business Analyst Exam

What You Need to Know

  • Passing score: 68%
  • Assessment: 60 multiple-choice and short-answer questions, plus up to 5 unscored pretest items
  • Time limit: 110 minutes
  • Exam fee: $180 USD

Keys to Passing

  • Complete 500+ practice questions
  • Score 80%+ consistently before scheduling
  • Focus on highest-weighted sections
  • Use our AI tutor for tough concepts

SAS Statistical Business Analyst Study Tips from Top Performers

1Logistic regression and model performance together account for 50% of the exam, so spend the most lab time in PROC LOGISTIC and on AUC/KS/lift/gains/Hosmer-Lemeshow.
2Practice PROC GLMSELECT with PARTITION FRACTION(VALIDATE=) and STOP=VALIDATE so that honest assessment becomes second nature.
3Memorize the WOE and IV formulas, plus the IV interpretation bands (<0.02 useless, 0.1-0.3 medium, >0.5 leakage suspect).
4Be fluent with FIRTH, PRIOREVENT=, OFFSET=, and CLODDS= so you can handle rare events and separation issues without stalling.
5Know how to use STORE OUT= with PROC PLM RESTORE= and the CODE FILE= statement to deploy a model into production scoring code.

Frequently Asked Questions

How many questions are on the SAS A00-240 exam?

The Statistical Business Analyst exam has 60 scored multiple-choice and short-answer questions, plus up to 5 unscored pretest items, completed in 110 minutes.

What is the passing score for A00-240?

Candidates must score at least 68% to pass the SAS Statistical Business Analyst Using SAS 9 exam.

How much does the SAS Statistical Business Analyst exam cost?

The exam fee is $180 USD worldwide. Academic discounts are available for qualifying students and educators through the SAS Academic program.

Which SAS PROCs does A00-240 cover most heavily?

PROC GLM (ANOVA), PROC REG and PROC GLMSELECT (linear regression and selection), and PROC LOGISTIC and PROC HPLOGISTIC (logistic regression and scoring) make up the majority of the exam.

Are practical scorecard topics on the exam?

Yes. WOE, Information Value, partitioning, oversampling, prior-probability calibration, lift/gains/KS/ROC, decision matrices, and score code generation are all in scope under the input preparation and model performance domains.

What is the recommended preparation path?

SAS recommends Statistics 1 and Statistics 2 plus Predictive Modeling Using Logistic Regression coursework, combined with hands-on SAS programming on PROC GLM, PROC REG, PROC GLMSELECT, and PROC LOGISTIC.