SAS Data Scientist Mock Tests

Certification Structure & Exam Pathway

The SAS Certified Data Scientist designation requires passing multiple exams across distinct core domains. The curriculum is structurally split into two primary pillars: Data Curation/Preparation and Advanced Analytics/Machine Learning.

[ SAS Certified Data Scientist ]

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┌────────────────────────────────┴────────────────────────────────┐

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[ Pillar 1: Data Curation & Big Data ] [ Pillar 2: Advanced Analytics & ML ]

├── Exam 1: Big Data Prep & Stats ├── Exam 3: Predictive Modeling (Enterprise Miner)

└── Exam 2: Big Data Programming ├── Exam 4: Advanced Predictive Modeling

└── Exam 5: Text Analytics, Time Series & Optimization

Pillar 1: Data Curation & Big Data Preparation

Domain 1: Big Data Preparation, Statistics, and Visual Exploration

This domain validates the ability to ingest data, assess its quality, perform foundational statistical analysis, and visually isolate patterns or anomalies.

Data Ingestion and Exploration

Reading external data files (delimited, fixed-width, and unstructured text).

Using SAS Studio and SAS Enterprise Guide to explore data topology and distribution.

Identifying data anomalies, extreme values, missing patterns, and structural inconsistencies.

Data Quality and Transformation

Standardizing, parsing, and validating data using DataFlux Data Management Studio and Server.

Implementing data cleansing, profiling, and entity resolution rules.

Applying data masking and obfuscation techniques for sensitive regulatory compliance.

Foundational Statistics and Analytics

Calculating descriptive statistics, distribution metrics, and inferential indicators.

Conducting hypothesis testing (t-tests, ANOVA, Chi-Square tests of independence).

Evaluating linear correlation and understanding variance structures using PROC STAT and PROC GLM.

Visual Data Exploration

Designing interactive exploratory dashboards in SAS Visual Analytics.

Leveraging SAS Visual Statistics to evaluate basic linear models, generalized linear models (GLMs), and regression splines dynamically.

Domain 2: Big Data Programming and Loading (Hadoop & Ecosystem Integration)

This domain evaluates a data scientist's capability to operate within large-scale distributed frameworks, managing processing tasks between SAS and Hadoop ecosystems.

SAS and Hadoop Ecosystem Integration

Configuring and managing librefs using the SAS/ACCESS Interface to Hadoop.

Executing implicit and explicit SQL Pass-Through queries to process data directly inside Hadoop.

Utilizing the DS2 programming language for parallelized, multi-threaded execution on cluster nodes.

Hadoop Ecosystem Mechanics (Hive, Pig, MapReduce)

Writing HiveQL queries via SAS to extract and manipulate large-scale tabular datasets.

Understanding Pig Latin scripts for complex data transformation pipelines.

Moving data dynamically between the local SAS File System and the Hadoop Distributed File System (HDFS).

In-Memory Big Data Acceleration

Using SAS Data Loader for Hadoop to direct extract, transform, and load (ETL) actions on a cluster.

Loading distributed data into memory for high-performance processing via SAS In-Memory Statistics.

Pillar 2: Advanced Analytics & Machine Learning

Domain 3: Predictive Modeling Using SAS Enterprise Miner

This domain covers the complete data mining lifecycle using a structured methodology (such as SEMMA: Sample, Explore, Modify, Model, Assess).

Data Preparation and Feature Engineering

Handling missing values through imputation techniques (mean, median, distribution-based, or model-driven).

Variable transformation (logarithmic, binning, optimal binning, and normalization).

Feature selection methods: filtering, forward/backward/stepwise selection, and variable clustering.

Core Predictive Modeling Algorithms

Decision Trees: Splitting criteria (Gini, Chi-Square, Information Gain), pruning strategies, and treating missing values in tree node assignments.

Regression Models: Linear and Logistic Regression formulation, interpreting odds ratios, and handling multicollinearity.

Neural Networks: Architecture setups (multilayer perceptrons), activation functions, and backpropagation optimization.

Model Evaluation and Selection

Using validation and test datasets to prevent overfitting.

Interpreting assessment metrics: Lift charts, ROC curves, Area Under the Curve (AUC), Misclassification Rate, and Profit/Loss matrices.

Comparing competing models using the Model Comparison node in SAS Enterprise Miner.

Domain 4: Advanced Predictive Modeling & Machine Learning

This domain expands on non-parametric algorithms, ensemble methods, and modern machine-learning models executed within modern analytics spaces.

Ensemble and Advanced Machine Learning Methods

Random Forests & Gradient Boosting: Tuning hyperparameters (number of trees, learning rate, sampling rates) using PROC HPFOREST and PROC GRADBOOST.

Support Vector Machines (SVM): Linear vs. non-linear kernels, margin maximization, and slack variables.

Clustering and Unsupervised Learning: K-means clustering, hierarchical clustering, and principal component analysis (PCA) for dimensionality reduction.

Open-Source Integration and Model Interoperability

Calling R packages and executing Python scripts seamlessly within the SAS environment.

Integrating open-source packages within SAS Enterprise Miner nodes.

Exporting R/Python models into production-ready deployment assets.

Generating score code (DATA step or DS2) for open-source models to run natively in SAS.

Domain 5: Text Analytics, Time Series, Experimentation, and Optimization

This domain deals with specialized analytics fields including unstructured textual processing, temporal forecasting, and business optimization strategies.

Text Mining and Natural Language Processing (NLP)

Converting unstructured text documents (PDFs, Word, Web logs) into structured SAS datasets.

Text parsing and filtering: stop lists, stemming, parts-of-speech tagging, and entity extraction.

Topic extraction (Singular Value Decomposition) and document clustering into homogeneous categories using SAS Text Miner.

Time Series Analysis and Forecasting

Creating structured, equally spaced time-series data from transactional logs.

Identifying temporal variations: trend, seasonality, cyclical behaviors, and calendar events.

Diagnosing, fitting, and interpreting Exponential Smoothing Models (ESM), ARIMAX models, and Unobserved Component Models (UCM) using SAS/ETS.

Operations Research and Mathematical Optimization

Formulating business objectives into linear, non-linear, and mixed-integer linear programming (MILP) equations.

Solving complex optimization models using the OPTMODEL procedure in SAS/OR.

Conducting Data Envelopment Analysis (DEA) for efficiency and productivity assessments.

Exam Design and Testing Format

Question Types: Multiple-choice, fill-in-the-blank, and performance-based practical coding scenarios (where candidates modify or write SAS code in a live, sandboxed lab environment).

Software Proficiency Tested: Base SAS, SAS Enterprise Guide, SAS Enterprise Miner, SAS Text Miner, SAS/STAT, SAS/ETS, SAS/OR, SAS Visual Analytics, and SAS Viya open-source engines.