Study on a laptop at noon, a phone on the train, or a printed PDF where the Wi-Fi never reaches. In 2026, LatestCram's A00-255 practice questions for SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 fit the 87-question bank around your day, not the other way around.
SASInstitute A00-255 Exam Overview:
| Certification Vendor: | SAS Institute |
|---|---|
| Exam Name: | SAS Predictive Modeling Using SAS Enterprise Miner 14 |
| Exam Number: | A00-255 |
| Exam Price: | USD 250 |
| Certificate Validity Period: | No expiration |
| Exam Duration: | 165 minutes |
| Passing Score: | 725/1000 |
| Available Languages: | English |
| Related Certifications: | SAS Certified Advanced Analytics Professional SAS Certified Data Scientist SAS Certified Predictive Modeler Using SAS Enterprise Miner 14 |
| Real Exam Qty: | 55-60 |
| Exam Format: | Performance-based, Short-answer, Multiple-choice |
| Sample Questions: | ![]() |
| Exam Way: | Computer-based exam delivered through Pearson VUE testing centers and online proctored testing. |
| Pre Condition: | No mandatory prerequisite. Candidates should have hands-on experience with SAS Enterprise Miner and predictive modeling concepts. |
| Official Syllabus URL: | https://www.sas.com/en_us/certification/credentials/advanced-analytics/predictive-modeler.html |
SASInstitute A00-255 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Sources | 20-25% | - Prepare Source Data
|
| Topic 2: Pattern Analysis | 10-15% | - Segmentation and Association Analysis
|
| Topic 3: Building Predictive Models | 35-40% | - Model Development
|
| Topic 4: Predictive Model Assessment and Implementation | 25-30% | - Model Deployment
|
The SASInstitute A00-255 Exam, Explained
The A00-255 exam is SASInstitute's formal test for the SAS Institute SAS certification, a credential at the Professional level. It verifies that you can apply the SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 objectives in realistic, scenario-driven situations — the difference between having read about the work and being able to do it. It also connects to a broader certification path covering SAS Certified Predictive Modeler Using SAS Enterprise Miner 14, SAS Certified Advanced Analytics Professional, SAS Certified Data Scientist, so the study effort compounds beyond a single exam. That is why preparing through realistic questions beats re-reading chapters every time.
The SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 blueprint spans 4 domains, led by Predictive Model Assessment and Implementation (25-30%), Building Predictive Models (35-40%), and Pattern Analysis (10-15%). Treat the percentages as a study budget: the heavier the domain, the larger its share of your week. The complete topic list sits in the exam topics section above — build your plan on it, not on guesswork.
The SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 exam packs 55-60 questions into 165 minutes. Turn that ratio into a plan before test day: minutes divided by questions gives your per-item budget, and anything consuming double its budget gets marked and revisited at the end. Practice the rhythm against a real timer — every LatestCram engine session enforces one — until finishing with spare review time is your default.
SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 requires 725/1000 to pass, and every attempt costs USD 250 — retakes included, at full price. The smart economics are simple: drill with LatestCram practice questions until your timed scores clear the mark across several consecutive sessions, then book the seat. Over-preparation is cheaper than re-registration.
SASInstitute lists the SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 question formats as: Multiple-choice, Short-answer, Performance-based. Format shapes strategy — locate the actual question sentence inside long scenario stems before reading details, and double-check every selection on multi-answer items. LatestCram's 87 practice questions exercise these formats so thoroughly that on exam day, only the content is unfamiliar.
You can sit the A00-255 exam in English. Choose whichever language keeps your comprehension fastest under the clock — slow reading is a hidden tax on every question. Testing in English? Daily sessions with LatestCram's English A00-255 practice questions double as focused terminology training.
The certification earned through the SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 exam stays valid for No expiration. Put the expiry date on your calendar the day you pass, and research the recertification path before it looms — a planned renewal is an errand, while an expired credential is a project. SASInstitute sets these policies, so verify the current rules on the official certification page as your window approaches.
All three versions carry the same 87 expert-written questions — you are choosing a study environment, not a content tier:
- PDF version — printable, expert-prepared, instantly downloadable, readable anywhere and anytime. Includes 365 days of free updates and a free demo.
- Desktop Test Engine — Windows software simulating the real exam environment, with two practice modes, offline access, and unlimited installations.
- Online Test Engine — runs in any browser on Windows, Mac, Android, and iOS, with test history and performance review built in.
Plenty of candidates mix formats — the PDF for reading, an engine for timed rehearsal.
Yes. LatestCram's free SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 PDF demo contains real sample questions with complete verified answers, so you can judge the quality yourself. Every purchase includes 365 days of free updates, and renewing after expiry costs 50% of the regular price from your member zone. Inspect first — material this confident has nothing to hide.
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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:
Assume the Target has an event proportion of 2% in the original data. Which of the following property values should be used in the Sample node of SAS Enterprise Miner to create a sample from that data with a balanced 50/50 split for Target?
Select one:
Response:
- A. Sample Method: Random and Criterion: Equal
- B. Sample Method: Stratify and Criterion: Proportional
- C. Sample Method: Stratify and Criterion: Equal
- D. Sample Method: Random and Criterion: Proportional
Correct Answer: C 🗳️
Perform these tasks in SAS Enterprise Miner:
* Continue to use the same diagram. Define and create the data set CREDIT_SCORE for scoring. The variables (their roles and measurement levels) in the CREDIT_SCORE data should be set as identical to those in the CREDIT data. The only exception is that the scoring data does not have a TARGET variable.
* Find the best model out of Decision Tree, Decision Tree (3-way), Regression, and Neural Network as defined by each of the four model's overall performance in the validation data measured by average squared error. Now, use this best model to score the CREDIT_SCORE data.
CREDIT SCORE:
The median of the predicted probabilities of TARGET=1 in the scoring data is in which of the following ranges?
Response:
- A. 0.50-0.849999
- B. 0.85 or more
- C. less than 0.149999
- D. 0.15-0.499999
Correct Answer: C 🗳️
Which method of input selection for regression analysis evaluates the statistical significance of the total model to see if it improves on the baseline as the variables are added and once no further improvement is made then variable selection ends?
Select one:
Response:
- A. Forward
- B. Simple
- C. Backward
- D. Stepwise
Correct Answer: A 🗳️
Transformation of input variables to make their distributions more symmetric will likely have what impact in a logistic regression?
Select one:
Response:
- A. create convergence problems in maximum likelihood estimation
- B. increase the performance of logistic regression
- C. neither increase nor decrease the performance of logistic regression
- D. decrease the performance of logistic regression
Correct Answer: B 🗳️
Perform these tasks in SAS Enterprise Miner:
* Add a Decision Tree node, as shown below. (Make sure you use only default options in the Decision Tree node.)
* Run the Decision Tree node.
In the training data set, consider only those observations for which the actual value of the target variable equals 1, TARGET=1. What percentage of these observations is being correctly predicted by the decision tree?
Response:
- A. 14.6216
- B. 66.6667
- C. 15
- D. 80.3571
Correct Answer: C 🗳️
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