Here are the sample questions which will help you be familiar with SAS Certified ModelOps Specialist (A00-440) exam style and structure. We encourage you to try our Demo SAS ModelOps Specialist Certification Practice Exam to measure your understanding of exam structure in an environment which simulates the SAS Managing the Model Life Cycle using ModelOps Certification test environment.
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SAS A00-440 Sample Questions:
01. What is a cause of bias in a model that is identified during peer review?
b) Poor sampling method
c) A low p-value
d) High misclassification
02. A model is created to predict loan delinquency at the bank. After model registration, the validator is asked to assess the model before it is put into production.
Which criteria should the validator use to make the go/no go decision?
a) Model qualitative characteristics assessed according to validator expertise.
b) Model quantitative characteristics estimated using a validation dataset.
c) Model performance measured in scoring latency.
d) Model quantitative characteristics estimated using the training dataset.
03. What is imperative to ensure the success of the ModelOps framework?
a) Set up knowledge sharing sessions around technology and best practices.
b) Confirm that everyone understands the ModelOps process and their role within.
c) Establish collaboration between IT/Operations, modelers, and the business team.
d) Provide job rotations to learn how other teams involved in the process work.
04. Scoring is configured to run on-prem for the sake of _______.
05. What is the first evaluation hurdle to overcome for a model to continue in the tournament?
a) Simplicity of deployment
b) Fit statistics and scoring overlay charts
c) CPU time and elapsed run time
d) Model input variables
06. Outlining all feedback loops and decisioning validation metrics is crucial in determining which step?
a) Business review process
b) Analytics peer review process
c) IT/operations review process
d) Business process workflow
07. What is the process to test and evaluate a model's accuracy on data from a subsequent time window called?
a) Out-of-bag testing
b) Out-of-time testing
c) Out-of-sample testing
d) Out-of-training testing
08. What is a characteristic of an ensemble modeling technique?
a) The posterior probabilities of the individual models are combined.
b) Interpretability techniques are incompatible with ensemble models.
c) Ensemble models consistently achieve better performance than individual models.
d) The voting function is done using singular value decomposition.
09. A model KPI has degraded below the acceptable threshold over the last several periods while the input variable distribution has remained stable. What is the next step in the process?
a) Reach out to the data engineer and ask for a correction in the data pipeline.
b) Consider additional variables and alternate modeling algorithms.
c) Continue tracking model performance and reassess during the next time period.
d) Build and deploy a better neural network model instead of the current algorithm.
10. What is the assumption that the variance of a dependent variable is not equal across all values of the independent variable referred to as?
a) Residual Error
Answer: a, b
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