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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. Data integration, joining, merging, and filtering 2. Groupby, apply, and aggregation operations 3. cuDF vs pandas API mapping and usage - Distributed computing with Dask 1. Dask-cuDF for parallel data processing 2. Scaling data operations across multiple GPUs - Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency
Topic 2: GPU and Cloud Computing	16%	- Performance optimization 1. Mixed precision and bottleneck analysis 2. Single and multi-GPU performance optimization 3. Memory profiling with DLProf - GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science - GPU resource management 1. Efficient GPU resource allocation and scheduling - Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud
Topic 3: MLOps	19%	- Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools - 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
Topic 4: Data Preparation	17%	- GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet - Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables - Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance
Topic 5: Data Analysis	14%	- Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - 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
Topic 6: Machine Learning	15%	- 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. Training models using cuML and GPU-accelerated XGBoost 2. Multi-GPU training strategies 3. Selection of appropriate algorithms for GPU execution - Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q99-Q104):

NEW QUESTION # 99
Which of the following best describes the purpose of the NVIDIA TensorRT library?

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

Answer: B

NEW QUESTION # 100
You are training a convolutional neural network (CNN) model with a large dataset on a single GPU.
The model is consuming too much GPU memory, and training is slow.
Which of the following techniques would help you reduce GPU memory consumption while maintaining or improving the efficiency of training? (Select two)

A. Use batch normalization to reduce memory usage by skipping some of the computations during training.
B. Use automatic differentiation for every operation to save memory during the backpropagation phase.
C. Implement mixed precision training to lower memory usage and speed up computation without losing accuracy.
D. Decrease the size of the model by reducing the number of layers or the number of filters per layer.
E. Use a smaller batch size to reduce the memory requirements per training iteration, allowing for faster training.

Answer: C,E

NEW QUESTION # 101
You are tasked with optimizing the performance of a large-scale data science project that involves deep learning models on a cloud infrastructure. Your organization is using GPUs for model training.
Which of the following strategies would be the most effective in optimizing GPU performance for data science tasks? (Select two)

A. Utilize multi-GPU training to parallelize the workload, reducing training time.
B. Overclock the GPU to achieve higher computational speeds and improve training times.
C. Optimize GPU performance by limiting the number of threads running on each GPU.
D. Use a single cloud instance with the largest GPU available to ensure maximum performance.
E. Use larger batch sizes to make better use of GPU memory during model training.

Answer: A,E

NEW QUESTION # 102
You are optimizing a data pipeline for a large-scale machine learning project using NVIDIA RAPIDS and Apache Spark. The pipeline performs many expensive shuffle operations.
Which of the following is the most effective method to reduce shuffle and improve performance using NVIDIA technologies?

A. Use RAPIDS cuDF to repartition the data in the GPU memory after each shuffle.
B. Use RAPIDS cuDF to perform in-memory data processing on GPU before shuffling to avoid network communication.
C. Store data in HDFS before performing shuffle operations to reduce GPU memory overhead.
D. Implement a custom shuffle partitioning scheme using NVIDIA DALI for more control over data partitioning during shuffle operations.

Answer: B

NEW QUESTION # 103
You are working with a large dataset in a cloud environment for a deep learning model. The dataset consists of several features including numerical values, categorical data, and timestamps.
Which of the following choices would result in the most efficient use of GPU and cloud resources when determining the optimal data type for each feature? (Select three)

A. Use datetime64[ns] for timestamp features to ensure high precision.
B. Use int32 for all numerical features to save memory.
C. Use object data types for categorical features to avoid type conversion overhead.
D. Use float64 for all numerical features to ensure maximum precision.
E. Use int8 for categorical features where there are fewer than 256 categories.

Answer: A,B,E

NEW QUESTION # 104
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