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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues - Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling - Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet
Topic 2: Machine Learning	15%	- Model training with GPU acceleration 1. Multi-GPU training strategies 2. Selection of appropriate algorithms for GPU execution 3. Training models using cuML and GPU-accelerated XGBoost - Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts - Feature engineering and hyperparameter tuning 1. Batching and memory-efficient training methods 2. Feature engineering for ML models 3. Hyperparameter tuning techniques
Topic 3: 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
Topic 4: GPU and Cloud Computing	16%	- Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud - GPU resource management 1. Efficient GPU resource allocation and scheduling - 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. GPU architecture fundamentals for data science 2. CPU vs GPU workloads and memory transfer optimization
Topic 5: Data Analysis	14%	- Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph
Topic 6: MLOps	19%	- Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation - Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows - Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - Experiment tracking 1. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q143-Q148):

NEW QUESTION # 143
When performing benchmarking and optimization for GPU-accelerated workflows, which of the following tools is best suited for analyzing the memory utilization and computational efficiency of deep learning models running on Nvidia GPUs?

A. Nvidia TensorRT
B. Nvidia Nsight Compute
C. Nvidia CUDA Profiler
D. Nvidia Riva

Answer: B

NEW QUESTION # 144
You are working with a dataset containing billions of rows and need to perform data transformations, aggregations, and joins efficiently on a single-node GPU-enabled workstation.
Which NVIDIA technology is best suited to optimize performance for these operations?

A. NVIDIA RAPIDS cuDF to leverage GPU acceleration for large-scale DataFrame operations.
B. NVIDIA Nsight Compute to profile and optimize the performance of GPU-based aggregations.
C. NVIDIA TensorRT to optimize DataFrame transformations and aggregations using deep learning.
D. NVIDIA Triton Inference Server to accelerate data processing workflows on a single GPU.

Answer: A

NEW QUESTION # 145
You need to train a deep learning model using PyTorch on a dataset too large for a single GPU. You decide to use Dask with NVIDIA GPUs for multi-GPU scaling.
Which approach is the most effective for distributing the workload?

A. Use Dask's built-in deep learning API to automatically distribute PyTorch models across GPUs
B. Use Dask.delayed to wrap PyTorch training functions and schedule them across multiple GPUs
C. Use Dask-CUDA workers with PyTorch's DistributedDataParallel (DDP) for training across multiple GPUs
D. Use Dask Bag to shard the dataset and train separate PyTorch models on each shard

Answer: C

NEW QUESTION # 146
You are training a deep learning model on a large dataset. Initially, you train the model on a single GPU and achieve a training time of 10 hours. To speed up training, you switch to a multi-GPU setup with four GPUs. However, after testing, you notice that the training time is only reduced to 3.5 hours instead of the expected 2.5 hours (a linear speedup).
What is the most likely reason for this sublinear speedup?

A. The learning rate is too low, causing slower convergence despite multiple GPUs.
B. The optimizer is not designed for multi-GPU training, causing inefficiency.
C. Increased memory bandwidth usage causes a bottleneck in the system.
D. Data transfer and communication overhead between GPUs limit scalability.

Answer: D

NEW QUESTION # 147
A data scientist is training a deep learning model and wants to find the best learning rate to optimize convergence speed and generalization. The scientist tests different values: A very small learning rate (0.00001) results in slow convergence.
A very large learning rate (10) causes the model loss to fluctuate wildly and not converge.
Which of the following strategies is the most effective way to optimize the learning rate dynamically during training?

A. Decrease the learning rate to zero at the end of training (learning rate scheduling)
B. Use learning rate warm-up followed by decay
C. Use the same learning rate for all layers in a deep neural network
D. Use a fixed learning rate chosen through trial and error

Answer: B

NEW QUESTION # 148
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