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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Machine Learning	15%	- Model training with GPU acceleration 1. Selection of appropriate algorithms for GPU execution 2. Multi-GPU training strategies 3. Training models using cuML and GPU-accelerated XGBoost - Feature engineering and hyperparameter tuning 1. Hyperparameter tuning techniques 2. Feature engineering for ML models 3. Batching and memory-efficient training methods - Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch
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
Data Manipulation and Software Literacy	19%	- Software literacy and development tools 1. Python, NumPy, pandas, Jupyter proficiency 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) - GPU-accelerated data manipulation using cuDF 1. Data integration, joining, merging, and filtering 2. cuDF vs pandas API mapping and usage 3. Groupby, apply, and aggregation operations - Distributed computing with Dask 1. Dask-cuDF for parallel data processing 2. Scaling data operations across multiple GPUs
MLOps	19%	- Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools - 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
Data Preparation	17%	- Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - 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 - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines
GPU and Cloud Computing	16%	- 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 - Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud - Performance optimization 1. Memory profiling with DLProf 2. Single and multi-GPU performance optimization 3. Mixed precision and bottleneck analysis

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q173-Q178):

NEW QUESTION # 173
A team of data scientists needs to deploy a machine learning model that depends on specific versions of CUDA and TensorFlow, ensuring it runs consistently across different machines without manually configuring each system.
Which of the following approaches best ensures consistency while leveraging NVIDIA GPUs?

A. Running the model in a local Python virtual environment and copying dependencies manually
B. Using NVIDIA Docker (nvidia-docker) to containerize the model and manage GPU dependencies
C. Using Docker without GPU support and relying on CPU fallback when running TensorFlow
D. Compiling all dependencies into the host machine and using system-wide installations

Answer: B

NEW QUESTION # 174
You are working on a machine learning pipeline using NVIDIA RAPIDS cuML and need to standardize the dataset to ensure that all features have a mean of 0 and a standard deviation of 1.
Which of the following methods should you use to achieve this in cuML?

A. cuml.preprocessing.MinMaxScaler()
B. cuml.preprocessing.StandardScaler()
C. cuml.preprocessing.OneHotEncoder()
D. cuml.preprocessing.LabelEncoder()

Answer: B

NEW QUESTION # 175
You are using RAPIDS cuML to train a regression model on a dataset with features of varying scales (temperature in Celsius, revenue in thousands, customer age). To improve model performance, you decide to standardize the data.
Which approach correctly standardizes the data using NVIDIA technologies?

A. Use numpy.mean() and numpy.std() to manually standardize the dataset before feeding it into the GPU.
B. Use cuml.PCA() to reduce the dimensionality of the dataset, which also standardizes feature variance.
C. Use cuml.StandardScaler() to transform the features to have a mean of zero and a standard deviation of one.
D. Use cuml.MinMaxScaler() to scale the features to a range of [0,1] without adjusting for mean and variance.

Answer: C

NEW QUESTION # 176
Which of the following techniques are best suited for efficiently processing and organizing large datasets using NVIDIA technologies? (Select two)

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

Answer: A,B

NEW QUESTION # 177
You are tasked with designing an ETL workflow for a large-scale data processing pipeline using NVIDIA technologies. You need to ensure that the extraction, transformation, and loading phases are optimized for performance using hardware acceleration.
Which of the following NVIDIA technologies would be most suitable for accelerating the ETL process?

A. RAPIDS
B. TensorRT
C. CUDA
D. TensorFlow

Answer: A

NEW QUESTION # 178
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