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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: MLOps	19%	- Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation - Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - Experiment tracking 1. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management
Topic 2: Data Preparation	17%	- GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables - Data loading and preprocessing 1. NVIDIA DALI for high-performance data loading 2. Handling class imbalance and generating synthetic data
Topic 3: Machine Learning	15%	- Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts - Feature engineering and hyperparameter tuning 1. Feature engineering for ML models 2. Hyperparameter tuning techniques 3. Batching and memory-efficient training methods - Model training with GPU acceleration 1. Multi-GPU training strategies 2. Selection of appropriate algorithms for GPU execution 3. Training models using cuML and GPU-accelerated XGBoost
Topic 4: GPU and Cloud Computing	16%	- Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - GPU resource management 1. Efficient GPU resource allocation and scheduling - GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science - Performance optimization 1. Memory profiling with DLProf 2. Single and multi-GPU performance optimization 3. Mixed precision and bottleneck analysis
Topic 5: Data Analysis	14%	- Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph - Visualization 1. Selecting appropriate plots for different analysis goals 2. Visualizing data using Plotly and Matplotlib - Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets
Topic 6: Data Manipulation and Software Literacy	19%	- GPU-accelerated data manipulation using cuDF 1. Data integration, joining, merging, and filtering 2. Groupby, apply, and aggregation operations 3. cuDF vs pandas API mapping and usage - Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency - Distributed computing with Dask 1. Dask-cuDF for parallel data processing 2. Scaling data operations across multiple GPUs

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q72-Q77):

NEW QUESTION # 72
Which of the following techniques are best suited for efficiently processing and organizing large datasets using NVIDIA technologies? (Select two)

A. Using cuDF for GPU-accelerated DataFrame operations
B. Using TensorFlow with custom data pipelines for data loading
C. Leveraging Dask for distributed GPU-based parallel computing
D. Storing data directly on an HDD and processing with GPUs

Answer: A,C

NEW QUESTION # 73
A machine learning engineer is working with a 1 TB dataset stored in Apache Parquet format and wants to analyze the data for patterns before building a model. The engineer is considering various acceleration methods.
Which of the following approaches would be the best choice for efficient analysis?

A. Load the dataset into a relational database and query it using simple SQL statements.
B. Convert the Parquet file to a Pandas DataFrame and perform analysis using Pandas functions.
C. Use a GPU-accelerated library such as RAPIDS cuDF to load and process the Parquet file efficiently.
D. Read the Parquet file line by line using Python's built-in file handling functions to save memory.

Answer: C

NEW QUESTION # 74
A machine learning team has deployed a fraud detection model in production, and they want to monitor its performance over time to detect model drift and performance degradation.
Which monitoring strategy is most effective for this use case?

A. Logging only inference latency without tracking model predictions
B. Using a static dataset for monitoring instead of real-time production data
C. Disabling logging to reduce system overhead and avoid storage costs
D. Implementing model performance tracking with drift detection metrics

Answer: D

NEW QUESTION # 75
A data scientist wants to compare the performance of two different GPU-accelerated data science frameworks, NVIDIA RAPIDS (cuDF, cuML) and TensorFlow, for a tabular data classification task.
Which of the following approaches would be the best practice for designing an unbiased and effective benchmark?

A. Use TensorFlow's built-in training time metrics without comparing equivalent RAPIDS-based operations.
B. Run all benchmarks on a CPU to ensure fairness across frameworks.
C. Measure execution time and memory usage for each framework using NVIDIA Nsight Systems (nsys).
D. Ignore preprocessing and focus only on model training speed when comparing performance.

Answer: C

NEW QUESTION # 76
A team of data engineers is working on an Apache Spark-based distributed computing pipeline that leverages NVIDIA GPUs and RAPIDS. They notice that shuffle operations are causing significant slowdowns in performance.
Which optimization strategy should they implement to reduce shuffle impact?

A. Disable GPU memory caching to allow automatic CPU-based shuffle optimization.
B. Use Spark's default shuffle partitioning without any modification, as GPUs inherently optimize shuffle operations.
C. Store shuffle data in Apache Parquet format on disk for faster access and reduced memory overhead.
D. Use RAPIDS Spark-RAPIDS Plugin with GPU-accelerated caching to minimize redundant shuffle operations.

Answer: D

NEW QUESTION # 77
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