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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 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 - Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud
Topic 2: 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. Batching and memory-efficient training methods 3. Feature engineering for ML models - Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch
Topic 3: Data Preparation	17%	- Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues - Data loading and preprocessing 1. NVIDIA DALI for high-performance data loading 2. Handling class imbalance and generating synthetic data - Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling - GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet
Topic 4: Data Analysis	14%	- 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. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals
Topic 5: MLOps	19%	- 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. 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 - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management
Topic 6: 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. Scaling data operations across multiple GPUs 2. Dask-cuDF for parallel data processing - 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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q92-Q97):

NEW QUESTION # 92
You are tasked with designing and implementing a benchmark to compare the performance of different deep learning frameworks, including TensorFlow, PyTorch, and JAX, using NVIDIA GPUs.
Which of the following is the most effective approach to ensure an accurate and fair comparison?

A. Ensure identical hardware configurations, dataset preprocessing, and model architectures while leveraging NVIDIA's Nsight Systems and DLProf for analysis.
B. Compare training times only without considering throughput, power efficiency, or memory utilization.
C. Run each framework with default settings to compare their out-of-the-box performance without any optimizations.
D. Use mixed precision (FP16) training only in TensorFlow to maximize performance while keeping other frameworks at FP32.

Answer: A

NEW QUESTION # 93
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.LabelEncoder()
B. cuml.preprocessing.OneHotEncoder()
C. cuml.preprocessing.StandardScaler()
D. cuml.preprocessing.MinMaxScaler()

Answer: C

NEW QUESTION # 94
You are analyzing a transportation network where airports represent nodes and flight routes represent edges. You need to determine the most critical airports in the network based on how many shortest paths pass through them.
Which cuGraph centrality algorithm should you use for this task?

A. Closeness Centrality, as it measures how close an airport is to all other airports, making it the best indicator of global connectivity.
B. Triangle Count, as it determines the number of three-node cycles an airport is part of, indicating its importance in network structure.
C. Degree Centrality, as it measures the number of direct connections an airport has, indicating its importance in the network.
D. Betweenness Centrality, as it identifies airports that act as major transit hubs by measuring how frequently they appear in shortest paths.

Answer: D

NEW QUESTION # 95
You are working on an MLOps pipeline with a large dataset using NVIDIA technologies. You need to assess the memory size of the dataset before training the model to avoid memory overflow errors.
Which of the following is the best method for verifying the memory size of the dataset on the GPU using NVIDIA tools?

A. Load the dataset into a CPU-based pandas dataframe and use the pandas.memory_usage() function to estimate the dataset's size before transferring it to the GPU.
B. Use NVIDIA DALI to process the dataset and directly query the memory size through DALI's DataLoader function.
C. Use PyTorch's torch.cuda.memory_allocated() function to check the memory allocated on the GPU after the dataset is loaded.
D. Use cuDF to load the dataset into GPU memory and use the nvidia-smi tool to monitor GPU memory usage during dataset loading.

Answer: D

NEW QUESTION # 96
You are working on a large-scale machine learning pipeline that involves processing massive datasets using multiple GPUs on an NVIDIA DGX system. You choose to use Dask to enable efficient parallel processing across multiple GPUs.
Which of the following steps is essential to correctly configure Dask for multi-GPU acceleration?

A. Avoid using dask_cudf and instead rely on standard pandas DataFrames to ensure GPU-accelerated execution.
B. Use dask.distributed.Client() without specifying a scheduler to automatically detect available GPUs.
C. Assign computation tasks explicitly to CPUs using dask.config.set({'scheduler': 'threads'}) before using GPUs.
D. Use dask_cuda.LocalCUDACluster() to create a cluster of GPU workers and pass it to the Dask client.

Answer: D

NEW QUESTION # 97
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