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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
MLOps	19%	- Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows - Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation - Model deployment and serving 1. Production deployment strategies 2. Model saving, loading, and prediction generation - Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools
Data Preparation	17%	- Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance - 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 Analysis	14%	- Visualization 1. Selecting appropriate plots for different analysis goals 2. Visualizing data using Plotly and Matplotlib - Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis - Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting
Data Manipulation and Software Literacy	19%	- Software literacy and development tools 1. Python, NumPy, pandas, Jupyter proficiency 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) - Distributed computing with Dask 1. Dask-cuDF for parallel data processing 2. Scaling data operations across multiple GPUs - 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
GPU and Cloud Computing	16%	- Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud - 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 - GPU resource management 1. Efficient GPU resource allocation and scheduling
Machine Learning	15%	- Feature engineering and hyperparameter tuning 1. Hyperparameter tuning techniques 2. Feature engineering for ML models 3. Batching and memory-efficient training methods - 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 (Q207-Q212):

NEW QUESTION # 207
You need to deploy a containerized machine learning model that utilizes NVIDIA GPUs on a cloud- based Kubernetes cluster.
Which of the following steps is essential for ensuring proper GPU utilization inside a Docker container?

A. Install GPU drivers inside the container to ensure access to the host's hardware
B. Use the NVIDIA Container Toolkit (nvidia-docker2) to allow GPU access within Docker containers
C. Use a standard Python-based container image instead of an NVIDIA GPU-optimized image
D. Run the container using the default Docker runtime without any additional configurations

Answer: B

NEW QUESTION # 208
Which of the following is the most efficient way to implement data parallelism using Dask for multi- GPU scaling on an Nvidia platform?

A. Use Dask with the dask_cuda package to distribute computation across GPUs, ensuring that each GPU is responsible for a portion of the data, while using distributed.Client to connect the GPU workers.
B. Use Dask with the dask_gpu package to assign data chunks across GPUs manually, without utilizing the distributed.Client.
C. Use Dask on a single GPU machine with no GPU-specific optimization, treating the system as if it were CPU-only.
D. Use Dask with a single GPU, distributing data across multiple workers within the GPU without considering GPU memory limitations.

Answer: A

NEW QUESTION # 209
You are working with a large dataset that contains missing values in multiple columns. Your goal is to prepare this dataset for training a machine learning model on an NVIDIA GPU using RAPIDS.
Which of the following approaches is the most efficient method to handle missing values in this scenario?

A. Apply a deep learning-based imputation model before moving data to the GPU
B. Drop all rows containing missing values using Pandas before transferring data to the GPU
C. Convert the dataset to a NumPy array and manually replace missing values with the mean
D. Use fillna() with a fixed value on the GPU using cuDF

Answer: D

NEW QUESTION # 210
A data scientist is preparing a dataset containing numerical features with varying scales and distributions. Some features range from 0 to 1, while others have values ranging from -5000 to 5000. The dataset will be used in a machine learning model that relies on gradient-based optimization.
What is the best approach to standardizing the data to ensure uniformity across features?

A. Normalize the dataset by dividing each feature by its maximum absolute value
B. Use min-max scaling to transform all features into the range [0,1]
C. Keep the features as they are since machine learning models can handle varying scales naturally
D. Apply z-score normalization (standardization) to scale features to have a mean of 0 and a standard deviation of 1

Answer: D

NEW QUESTION # 211
A data scientist is training a deep learning model on an NVIDIA GPU but notices that the training speed is not significantly faster than when using a CPU.
Which of the following strategies is the best approach to fully utilize GPU acceleration and optimize training performance?

A. Disabling cuDNN optimizations to allow more flexibility in kernel execution
B. Increasing the batch size beyond the GPU's memory limit
C. Using mixed precision training with NVIDIA Tensor Cores
D. Using only CPU-based data augmentation to keep the GPU dedicated to training

Answer: C

NEW QUESTION # 212
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