Applied Data Science, GenAI & MLOps Engineering Topic-Wise Online Mock Tests & Free MCQ Practice
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Applied Data Science, GenAI & MLOps Engineering — Topic-Wise Mock Tests
Expand any module below to start a live timed MCQ exam across 84 Mock Test sets (max 2 free attempts per topic per day).
All Modules & Topics in Applied Data Science, GenAI & MLOps Engineering
NumPy Broadcasting & High-Performance Data Pipelines
Purpose: To understand and apply NumPy Broadcasting & High-Performance Data Pipelines in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Eliminating slow Python loops with contiguous C-array vectorization, so your code stays reliable, fast, and easy to maintain in production.
Pandas DataFrame Indexing, GroupBy Aggregations & Memory Optimization
Purpose: To understand and apply Pandas DataFrame Indexing, GroupBy Aggregations & Memory Optimization in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Clean messy multi-million-row CSV/Parquet datasets fast without exhausting RAM, so your code stays reliable, fast, and easy to maintain in production.
Analytical SQL for Data Scientists: CTEs, Window Functions & Funnel Queries
Purpose: To understand and apply Analytical SQL for Data Scientists: CTEs, Window Functions & Funnel Queries in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Extract ML-ready customer feature tables directly inside PostgreSQL/DuckDB, so your code stays reliable, fast, and easy to maintain in production.
High-Speed Lazy Dataframes with Polars & DuckDB Local Lakehouse
Purpose: To understand and apply High-Speed Lazy Dataframes with Polars & DuckDB Local Lakehouse in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Process 20GB+ datasets on a standard laptop using Rust/C++ vectorized query engines, so your code stays reliable, fast, and easy to maintain in production.
Automated Ingestion Pipelines, Pydantic Data Schemas & Idempotent Backfills
Purpose: To understand and apply Automated Ingestion Pipelines, Pydantic Data Schemas & Idempotent Backfills in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Ingest partner APIs and webhook JSON logs cleanly into partitioned feature tables, so your code stays reliable, fast, and easy to maintain in production.
Diagnostic EDA, Outlier Detection & Feature Correlation Forensics
Purpose: To understand and apply Diagnostic EDA, Outlier Detection & Feature Correlation Forensics in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Uncover data skew, Simpson’s paradox, and collinearity before fitting a single model, so your code stays reliable, fast, and easy to maintain in production.
Probability Distributions, Bayes’ Theorem & Confidence Intervals
Purpose: To understand and apply Probability Distributions, Bayes’ Theorem & Confidence Intervals in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Quantify uncertainty around churn rates, fraud probabilities, and demand forecasts, so your code stays reliable, fast, and easy to maintain in production.
Statistical A/B Testing: Sample Size Power, p-Values & Multiple-Testing Guardrails
Purpose: To understand and apply Statistical A/B Testing: Sample Size Power, p-Values & Multiple-Testing Guardrails in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Design valid online product experiments that distinguish real lift from random noise, so your code stays reliable, fast, and easy to maintain in production.
Causal Uplift Thinking, Guardrail Metrics & Network Interference Checks
Purpose: To understand and apply Causal Uplift Thinking, Guardrail Metrics & Network Interference Checks in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Ensure product launches improve net retention without hurting latency or support tickets, so your code stays reliable, fast, and easy to maintain in production.
ColumnTransformer Pipelines, Target Encoding & Leakage Prevention
Purpose: To understand and apply ColumnTransformer Pipelines, Target Encoding & Leakage Prevention in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Package preprocessing and modeling into a single serializable Scikit-Learn Pipeline, so your code stays reliable, fast, and easy to maintain in production.
Regularized Linear Models (Ridge, Lasso, ElasticNet) & Cross-Validation Strategies
Purpose: To understand and apply Regularized Linear Models (Ridge, Lasso, ElasticNet) & Cross-Validation Strategies in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Build fast, interpretable baseline models and choose time/group-aware CV splits, so your code stays reliable, fast, and easy to maintain in production.
Imbalanced Learning: PR-AUC, Cost-Sensitive Thresholds & Probability Calibration
Purpose: To understand and apply Imbalanced Learning: PR-AUC, Cost-Sensitive Thresholds & Probability Calibration in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Optimize fraud, medical, and churn classifiers where positive cases are < 2% of rows, so your code stays reliable, fast, and easy to maintain in production.
Gradient Boosting Math, Histogram Split Finding & XGBoost/LightGBM Tuning
Purpose: To understand and apply Gradient Boosting Math, Histogram Split Finding & XGBoost/LightGBM Tuning in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Master the #1 tabular ML algorithm family used across FinTech, retail, and ad-tech, so your code stays reliable, fast, and easy to maintain in production.
Bayesian Hyperparameter Optimization with Optuna & Early Stopping
Purpose: To understand and apply Bayesian Hyperparameter Optimization with Optuna & Early Stopping in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Search hyperparameter spaces intelligently in a fraction of GridSearch compute cost, so your code stays reliable, fast, and easy to maintain in production.
Model Explainability with TreeSHAP, Permutation Importance & Fairness Audits
Purpose: To understand and apply Model Explainability with TreeSHAP, Permutation Importance & Fairness Audits in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Generate regulator-ready per-customer prediction explanations and detect subgroup bias, so your code stays reliable, fast, and easy to maintain in production.
K-Means, HDBSCAN Density Clustering & Behavioral RFM Segmentation
Purpose: To understand and apply K-Means, HDBSCAN Density Clustering & Behavioral RFM Segmentation in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Group millions of unlabeled users or SKUs into actionable business segments, so your code stays reliable, fast, and easy to maintain in production.
PCA, UMAP Visualization & Unsupervised Anomaly Detection (Isolation Forest)
Purpose: To understand and apply PCA, UMAP Visualization & Unsupervised Anomaly Detection (Isolation Forest) in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Compress wide sensor/embedding spaces and catch novel zero-day anomalies without labels, so your code stays reliable, fast, and easy to maintain in production.
Time-Series Decomposition, Walk-Forward Backtesting & Multi-Step Forecasting
Purpose: To understand and apply Time-Series Decomposition, Walk-Forward Backtesting & Multi-Step Forecasting in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Forecast daily inventory demand, cloud spend, and revenue across multiple horizons, so your code stays reliable, fast, and easy to maintain in production.
PyTorch Tensors, Dynamic Autograd Graphs & Custom Dataset/DataLoader
Purpose: To understand and apply PyTorch Tensors, Dynamic Autograd Graphs & Custom Dataset/DataLoader in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Build GPU-accelerated neural training loops with clean data batching, so your code stays reliable, fast, and easy to maintain in production.
Optimizers (AdamW), Cosine LR Schedules, Mixed Precision (AMP) & Checkpointing
Purpose: To understand and apply Optimizers (AdamW), Cosine LR Schedules, Mixed Precision (AMP) & Checkpointing in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Train neural networks 2x faster in BF16/FP16 with reproducible model checkpoints, so your code stays reliable, fast, and easy to maintain in production.
Transfer Learning, Vision Backbones & ONNX Runtime Quantized Export
Purpose: To understand and apply Transfer Learning, Vision Backbones & ONNX Runtime Quantized Export in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Fine-tune pretrained backbones on small client datasets and serve them on low-cost CPUs, so your code stays reliable, fast, and easy to maintain in production.
Self-Attention Mechanics, Subword Tokenization & Encoder vs Decoder LLMs
Purpose: To understand and apply Self-Attention Mechanics, Subword Tokenization & Encoder vs Decoder LLMs in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Understand exactly how modern Transformers process context windows, tokens, and KV caches, so your code stays reliable, fast, and easy to maintain in production.
Dense Bi-Encoder Embeddings, Cosine Similarity & Cross-Encoder Reranking
Purpose: To understand and apply Dense Bi-Encoder Embeddings, Cosine Similarity & Cross-Encoder Reranking in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Map sentences, resumes, and products into semantic vector spaces, so your code stays reliable, fast, and easy to maintain in production.
Prompt Engineering Systems, Structured JSON Outputs & LoRA / QLoRA Fine-Tuning
Purpose: To understand and apply Prompt Engineering Systems, Structured JSON Outputs & LoRA / QLoRA Fine-Tuning in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Control LLM outputs deterministically and know when prompting beats fine-tuning, so your code stays reliable, fast, and easy to maintain in production.
Document Parsing, Structure-Aware Chunking & Metadata Enrichment
Purpose: To understand and apply Document Parsing, Structure-Aware Chunking & Metadata Enrichment in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Prepare PDFs, policies, and codebases into high-signal chunks that retrieve cleanly, so your code stays reliable, fast, and easy to maintain in production.
PostgreSQL pgvector, HNSW vs IVFFlat Indexes & Pre-Filtered Tenant Search
Purpose: To understand and apply PostgreSQL pgvector, HNSW vs IVFFlat Indexes & Pre-Filtered Tenant Search in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Run vector search right inside ACID PostgreSQL alongside relational RBAC permissions, so your code stays reliable, fast, and easy to maintain in production.
Hybrid Search: Combining Lexical Full-Text BM25 with Reciprocal Rank Fusion (RRF)
Purpose: To understand and apply Hybrid Search: Combining Lexical Full-Text BM25 with Reciprocal Rank Fusion (RRF) in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Handle exact SKU codes, error IDs, and semantic natural-language queries simultaneously, so your code stays reliable, fast, and easy to maintain in production.
Query Rewriting, Multi-Query Expansion & Conversational Memory Compression
Purpose: To understand and apply Query Rewriting, Multi-Query Expansion & Conversational Memory Compression in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Resolve follow-up pronouns (\"what about its refund fee?\") into standalone retrieval queries, so your code stays reliable, fast, and easy to maintain in production.
Strict Citation Grounding, Abstention Logic & Faithfulness Verification
Purpose: To understand and apply Strict Citation Grounding, Abstention Logic & Faithfulness Verification in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Ensure enterprise copilots cite exact source paragraphs and abstain when evidence is absent, so your code stays reliable, fast, and easy to maintain in production.
Prompt Injection Defense, PII Redaction & Semantic Response Caching
Purpose: To understand and apply Prompt Injection Defense, PII Redaction & Semantic Response Caching in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Protect GenAI endpoints against jailbreaks, data exfiltration, and runaway API bills, so your code stays reliable, fast, and easy to maintain in production.
Deterministic Function Calling, Tool Schemas & Human-in-the-Loop Approvals
Purpose: To understand and apply Deterministic Function Calling, Tool Schemas & Human-in-the-Loop Approvals in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Connect Gemini/LLMs safely to internal SQL, CRM, and ticketing APIs, so your code stays reliable, fast, and easy to maintain in production.
Multi-Step Graph Workflows (Router -> Retriever -> Verifier) vs Free-Form Agents
Purpose: To understand and apply Multi-Step Graph Workflows (Router -> Retriever -> Verifier) vs Free-Form Agents in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Design predictable state-machine AI workflows that hit 99% production reliability, so your code stays reliable, fast, and easy to maintain in production.
Automated Golden-Dataset Evals (RAGAS / LLM-as-Judge) in GitHub Actions CI
Purpose: To understand and apply Automated Golden-Dataset Evals (RAGAS / LLM-as-Judge) in GitHub Actions CI in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Prevent prompt edits or model upgrades from silently breaking accuracy, so your code stays reliable, fast, and easy to maintain in production.
Async FastAPI Model Serving, Lifespan Warmup & SSE Token Streaming
Purpose: To understand and apply Async FastAPI Model Serving, Lifespan Warmup & SSE Token Streaming in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Expose ML and GenAI pipelines as low-latency, high-concurrency production APIs, so your code stays reliable, fast, and easy to maintain in production.
Experiment Tracking, Model Registry (MLflow) & Reproducible Docker Packaging
Purpose: To understand and apply Experiment Tracking, Model Registry (MLflow) & Reproducible Docker Packaging in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Version datasets, hyperparameters, prompts, and container artifacts with full lineage, so your code stays reliable, fast, and easy to maintain in production.
Data/Concept Drift Detection (PSI), Token Cost Governance & Retraining Triggers
Purpose: To understand and apply Data/Concept Drift Detection (PSI), Token Cost Governance & Retraining Triggers in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Monitor live models after launch so silent distribution shifts never erode business ROI, so your code stays reliable, fast, and easy to maintain in production.
Chunking Strategies, Metadata Filtering & Hybrid Keyword + Vector Retrieval
Purpose: To understand and apply Chunking Strategies, Metadata Filtering & Hybrid Keyword + Vector Retrieval in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Retrieve exact enterprise policy clauses with high recall instead of noisy semantic-only matches, so your code stays reliable, fast, and easy to maintain in production.
Cross-Encoder Re-Ranking, Citation Enforcement & Prompt-Injection Guards
Purpose: To understand and apply Cross-Encoder Re-Ranking, Citation Enforcement & Prompt-Injection Guards in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Block jailbreak attempts and force LLM responses to cite verifiable source passages, so your code stays reliable, fast, and easy to maintain in production.
Automated RAG Evaluation (Faithfulness, Context Precision & Latency Budgets)
Purpose: To understand and apply Automated RAG Evaluation (Faithfulness, Context Precision & Latency Budgets) in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Catch prompt regressions and hallucination spikes automatically before shipping model updates, so your code stays reliable, fast, and easy to maintain in production.
Feature Skew Detection, Data Drift Alerts (PSI/KS) & Shadow Deployments
Purpose: To understand and apply Feature Skew Detection, Data Drift Alerts (PSI/KS) & Shadow Deployments in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Detect when live customer distributions diverge from training data before prediction accuracy degrades, so your code stays reliable, fast, and easy to maintain in production.
Async Batching, KV-Cache / Semantic Caching & Token Cost Governance
Purpose: To understand and apply Async Batching, KV-Cache / Semantic Caching & Token Cost Governance in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Cut LLM and ML inference latency and cloud GPU/API bills by up to 60% in production, so your code stays reliable, fast, and easy to maintain in production.
End-to-End ML & GenAI System Design Case Studies for Product Interviews
Purpose: To understand and apply End-to-End ML & GenAI System Design Case Studies for Product Interviews in real Applied Data Science, GenAI & MLOps Engineering systems — specifically: Design recommendation, fraud-scoring, and enterprise copilot architectures step-by-step, so your code stays reliable, fast, and easy to maintain in production.