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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Manipulation and Software Literacy	19%	- GPU-accelerated data manipulation using cuDF 1. cuDF vs pandas API mapping and usage 2. Groupby, apply, and aggregation operations 3. Data integration, joining, merging, and filtering - Software literacy and development tools 1. Python, NumPy, pandas, Jupyter proficiency 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) - Distributed computing with Dask 1. Scaling data operations across multiple GPUs 2. Dask-cuDF for parallel data processing
Topic 2: MLOps	19%	- Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows - Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools
Topic 3: 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. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting
Topic 4: 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. Batching and memory-efficient training methods 2. Hyperparameter tuning techniques 3. Feature engineering for ML models - Model training with GPU acceleration 1. Selection of appropriate algorithms for GPU execution 2. Training models using cuML and GPU-accelerated XGBoost 3. Multi-GPU training strategies
Topic 5: Data Preparation	17%	- GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling - Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues
Topic 6: GPU and Cloud Computing	16%	- Performance optimization 1. Memory profiling with DLProf 2. Single and multi-GPU performance optimization 3. Mixed precision and bottleneck analysis - 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. GPU architecture fundamentals for data science 2. CPU vs GPU workloads and memory transfer optimization

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q86-Q91):

NEW QUESTION # 86
You are working on an MLOps pipeline that involves loading a large dataset for training a deep learning model on an NVIDIA GPU. Before training, you need to ensure that the dataset fits within the available GPU memory.
Which of the following commands in Python using the pandas and numpy libraries can correctly determine the memory size of a dataset?

A. df.memory_usage(deep=True).sum()
B. sys.getsizeof(df)
C. np.array(df).nbytes
D. df.info(memory_usage='deep')

Answer: A

NEW QUESTION # 87
When utilizing GPU instances in a cloud environment for data science, which of the following are common considerations? (Select two)

A. Cost-effective GPU instances always provide the best performance
B. GPU performance can be degraded by network latency in distributed training
C. Cloud providers offer dedicated GPU instances for high-performance needs
D. GPUs are suitable for both training and inference tasks but not for preprocessing

Answer: B,C

NEW QUESTION # 88
A team is processing large-scale tabular data using cuDF and cuML on NVIDIA GPUs but is facing performance degradation.
Which of the following techniques would be the most effective in identifying and resolving bottlenecks in the pipeline?

A. Use nvprof or nsight compute to analyze kernel execution time and memory transfer efficiency.
B. Reduce the dataset size to a smaller sample to speed up processing.
C. Convert all datasets into pandas DataFrames for initial processing before moving to cuDF.
D. Increase GPU clock speed manually to force higher processing power.

Answer: A

NEW QUESTION # 89
You have a structured dataset containing 20 million records with missing values in several columns.
You need to fill missing values while ensuring that the approach is optimal for execution on NVIDIA GPUs.
Which method should you use?

A. Use cuDF's .fillna() method to replace missing values in GPU memory
B. Convert the dataset to a pandas DataFrame, fill missing values, and then convert it back to cuDF
C. Drop all missing values using .dropna() instead of filling them, as GPU memory is limited
D. Load the dataset into Modin with a Dask backend and use .fillna() for parallel execution

Answer: A

NEW QUESTION # 90
Which of the following best describes the purpose of the NVIDIA TensorRT library?

A. Optimizes and accelerates inference of trained models
B. Manages GPU resources for deep learning models
C. Provides hardware abstraction for AI model development
D. Accelerates training of neural networks

Answer: A

NEW QUESTION # 91
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