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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Machine Learning	15%	- Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch - Model training with GPU acceleration 1. Training models using cuML and GPU-accelerated XGBoost 2. Selection of appropriate algorithms for GPU execution 3. Multi-GPU training strategies - Feature engineering and hyperparameter tuning 1. Hyperparameter tuning techniques 2. Feature engineering for ML models 3. Batching and memory-efficient training methods
Data Preparation	17%	- Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - Data loading and preprocessing 1. NVIDIA DALI for high-performance data loading 2. Handling class imbalance and generating synthetic data
Data Analysis	14%	- Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - 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
MLOps	19%	- Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management - Experiment tracking 1. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware - Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation
Data Manipulation and Software Literacy	19%	- GPU-accelerated data manipulation using cuDF 1. Groupby, apply, and aggregation operations 2. cuDF vs pandas API mapping and usage 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
GPU and Cloud Computing	16%	- Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud - Performance optimization 1. Mixed precision and bottleneck analysis 2. Memory profiling with DLProf 3. Single and multi-GPU performance optimization - 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 (Q98-Q103):

NEW QUESTION # 98
You have developed a deep learning model using TensorFlow and trained it on an NVIDIA A100 GPU. The model is deployed in production and serves real-time inference requests. However, the inference latency is high, and you need to optimize performance without retraining the model.
Which of the following approaches is the most effective for optimizing inference performance using NVIDIA technologies?

A. Implement data augmentation techniques to improve inference efficiency.
B. Convert the model to ONNX format and use TensorRT for inference optimization.
C. Reduce the batch size to decrease computational overhead and improve latency.
D. Enable mixed precision training and retrain the model to improve inference speed.

Answer: B

NEW QUESTION # 99
You are running a data science project on a cloud environment, where you need to optimize the GPU utilization for real-time data processing tasks.
Which of the following practices should you consider to maximize GPU performance? (Select two)

A. Use data parallelism techniques to split the workload evenly across multiple GPUs.
B. Implement model inference in a batch mode rather than in a real-time streaming mode for better performance.
C. Prioritize using CPU for computationally expensive tasks and reserve GPU for I/O operations.
D. Scale the cloud infrastructure vertically by increasing the size of the GPU in use.
E. Use NVIDIA CUDA libraries optimized for data science tasks to accelerate computations.

Answer: A,E

NEW QUESTION # 100
You are working with a time-series dataset containing network traffic logs. You suspect the presence of anomalies, such as distributed denial-of-service (DDoS) attacks or sudden traffic surges.
Which machine learning approach is best suited for detecting these anomalies?

A. Linear Regression
B. Naive Bayes Classifier
C. Autoencoder-based Anomaly Detection
D. k-Nearest Neighbors (k-NN) for Classification

Answer: C

NEW QUESTION # 101
Which of the following Nvidia technologies is commonly used for deploying machine learning models in production environments, enabling scalable deployment and monitoring?

A. Nvidia Triton Inference Server
B. Nvidia Jetson Nano
C. Nvidia DeepStream
D. Nvidia CUDA

Answer: A

NEW QUESTION # 102
You are working on a large-scale machine learning workload that involves training a deep learning model using multiple GPUs. You want to leverage Dask to implement data parallelism efficiently using NVIDIA GPUs.
Which of the following approaches best achieves data parallelism in this context?

A. Use Dask with CuPy to distribute NumPy-based computations across multiple GPUs
B. Leverage Dask-CUDA to automatically assign computations to available GPUs using the worker pool
C. Run a single large Dask task on the CPU and use Dask-MPI for multi-GPU execution
D. Use Dask DataFrame to parallelize deep learning model training across multiple GPUs

Answer: B

NEW QUESTION # 103
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