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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
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. Hyperparameter tuning techniques 2. Batching and memory-efficient training methods 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
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 - Performance optimization 1. Single and multi-GPU performance optimization 2. Memory profiling with DLProf 3. Mixed precision and bottleneck analysis - GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science
Data Analysis	14%	- Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph - Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - Visualization 1. Selecting appropriate plots for different analysis goals 2. Visualizing data using Plotly and Matplotlib - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis
MLOps	19%	- Model deployment and serving 1. Production deployment strategies 2. Model saving, loading, and prediction generation - Experiment tracking 1. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware - Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows
Data Preparation	17%	- Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet - Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling - Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance
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. 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 (Q86-Q91):

NEW QUESTION # 86
You are analyzing a large-scale transportation network using cuGraph and notice that query times are longer than expected when running graph algorithms.
What is the best way to optimize graph processing performance using GPU-accelerated tools?

A. Store the graph in COO (Coordinate List) format instead of CSR (Compressed Sparse Row) format for faster traversal.
B. Convert the graph to CSR (Compressed Sparse Row) format before running computations to improve memory efficiency.
C. Use cugraph.to_directed() to convert the graph into a directed format, which improves GPU parallelism.
D. Use cugraph.filter_unconnected_nodes() to remove unconnected nodes before processing.

Answer: B

NEW QUESTION # 87
A data scientist is analyzing a large time-series dataset containing stock price movements of thousands of companies over a decade. The dataset is stored as a cuDF DataFrame and contains millions of rows. The scientist wants to visualize trends and patterns interactively while leveraging GPU acceleration.
Which of the following approaches is the most efficient for visualizing this time-series data?

A. Use cuXfilter with a cuDF DataFrame to generate interactive visualizations directly on the GPU.
B. Use matplotlib with plt.plot() while applying df.to_pandas() to convert data.
C. Convert the cuDF DataFrame to Pandas and use matplotlib for plotting.
D. Use seaborn with a sampled subset of the dataset to generate line plots.

Answer: A

NEW QUESTION # 88
You are setting up a deep learning model for training on a multi-GPU cluster. You want to maximize training efficiency while maintaining model convergence.
Which of the following strategies is most effective in ensuring efficient multi-GPU training?

A. Reduce batch size to ensure each GPU receives only a small portion of the training data.
B. Use Data Parallelism, where each GPU gets a different portion of the dataset, but gradients are averaged across all GPUs.
C. Train each GPU independently on a different dataset to reduce the communication bottleneck.
D. Use Model Parallelism, where different layers of the model are placed on different GPUs to reduce communication overhead.

Answer: B

NEW QUESTION # 89
You have a pandas DataFrame with a column containing floating-point numbers, but it takes up too much memory. You want to convert it into a lower-precision type using CuDF or pandas while ensuring computational efficiency.
Which function would you use?

A. df.astype('float16')
B. df['col'].apply(lambda x: np.float16(x))
C. df.to_float16()
D. df.convert_dtypes()

Answer: A

NEW QUESTION # 90
A data scientist is training a deep learning model on an NVIDIA GPU but is encountering out-of- memory (OOM) errors.
To optimize GPU memory usage while maintaining efficient training performance, which of the following strategies should they prioritize?

A. Storing all training data in GPU memory at once
B. Increasing batch size without adjusting the optimizer settings
C. Using single-precision (FP32) calculations for better accuracy
D. Using mixed precision training with automatic loss scaling

Answer: D

NEW QUESTION # 91
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