SAS Viya Programming

 SAS Viya Programming — Interview Q&A

---

✅ Q1: What is SAS Viya? How is it different from traditional SAS?

💡 Answer:
SAS Viya is a modern, cloud-native analytics platform designed for large-scale data processing, advanced analytics, AI/ML model building, and deployment. It’s built on top of CAS (Cloud Analytic Services), which allows distributed in-memory processing.

Unlike traditional SAS (SAS 9.x), Viya:

• Supports cloud deployment (Azure, AWS, GCP, Kubernetes).

• Integrates with open-source (Python, R, REST API) natively.

• Uses microservices for scalability and collaboration.

• Allows faster model building with Viya’s visual and code interfaces.

---

✅ Q2: What is CAS in SAS Viya?

💡 Answer:
CAS (Cloud Analytic Services) is the in-memory engine of SAS Viya. It allows distributed, parallel data processing and lets multiple users work on large datasets loaded into memory without reloading.

It supports:

• High-performance computing on big data.

• Advanced analytics (ML, AI, forecasting).

• Persistent or temporary in-memory tables.

---

✅ Q3: How do you load data into CAS?

💡 Answer:
You can load data into CAS using PROC CASUTIL, DATA Step, or SAS Studio. Example:

cas mySession;
libname casuser cas;

proc casutil;
   load data=sashelp.cars
   outcaslib="casuser"
   casout="cars_data"
   replace;
run;

This loads a SAS dataset into CAS memory so it can be processed in parallel.

---

✅ Q4: How do you build machine learning models in SAS Viya?

💡 Answer:
In SAS Viya, models are built using:

• PROC FOREST (Random Forest).

• PROC GRADBOOST (Gradient Boosting).

• PROC LOGISTIC (Logistic Regression).

• PROC CAS (direct CAS actions for fine control).

• Or Model Studio’s AutoML GUI.

Example: Gradient Boosting model for churn prediction:

proc gradboost data=casuser.customer_train;
    target churn / level=nominal;
    input age income tenure / level=interval;
    input plan_type region / level=nominal;
    savestate rstore=casuser.churn_model;
run;

---

✅ Q5: How do you save and deploy a model in SAS Viya?

💡 Answer:
After model training, models can be saved using savestate (for scoring) or astore:

proc astore;
   save rstore=casuser.churn_model
        out=casuser.deployed_churn_model;
run;

You can then deploy the model:

• Via SAS Model Manager for lifecycle governance.

• Through REST APIs for real-time scoring.

• As a batch-scoring job in production.

---

✅ Q6: How does SAS Viya integrate with open-source tools?

💡 Answer:
Viya integrates with Python, R, and REST APIs.

• You can call Viya models from Python using swat (SAS Scripting Wrapper for Analytics Transfer).

• Models trained in SAS can be scored in Python and vice-versa.

• Viya also offers AutoML pipelines through the SAS Visual Data Mining & Machine Learning (VDMML) suite.

---

✅ Q7: What are the benefits of using SAS Viya for large datasets?

💡 Answer:

• Distributed processing across multiple servers.

• In-memory table handling (no need for disk I/O).

• Parallel data manipulation and model training.

• Reduced data movement between storage and compute.

• Real-time model deployment with Model Manager.

---

✅ Q8: What are some best practices in SAS Viya programming?

💡 Answer:

• Use KEEP= and DROP= to limit unnecessary data movement.

• Leverage CASLIB effectively to organize data.

• Use save state to save and reuse trained models.

• Free up memory by dropping unused CAS tables.

• Write modular, reusable macros.

• Validate models with ROC, AUC, and Lift/Gain charts before deployment.

• Automate model monitoring in production.

---

✅ Q9: How do you manage model lifecycle in SAS Viya?

💡 Answer:
Model lifecycle is managed using SAS Model Manager:

• Models can be versioned, approved, and published to scoring environments.

• Supports monitoring of model drift.

• Provides governance for regulatory compliance.

• Allows both manual and automated redeployment.

---

✅ Q10: How do you handle security and access control in SAS Viya?

💡 Answer:

• Role-based access control via SAS Environment Manager.

• CASLIB permissions for datasets.

• OAuth and Single Sign-On (SSO) integration for enterprise-grade security.

• Sensitive data is masked or encrypted when shared between teams.

---

✅ Final Interview Closing Line:
"SAS Viya enables me to develop, train, validate, and deploy models efficiently in a cloud-ready environment, while collaborating seamlessly with both SAS and open-source ecosystems."