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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
MLOps	19%	- Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management - Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation - Experiment tracking 1. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware
Data Preparation	17%	- GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet - Data loading and preprocessing 1. NVIDIA DALI for high-performance data loading 2. Handling class imbalance and generating synthetic data - Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables
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 - Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - GPU architecture and fundamentals 1. GPU architecture fundamentals for data science 2. CPU vs GPU workloads and memory transfer optimization
Machine Learning	15%	- Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts - 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. Multi-GPU training strategies 2. Training models using cuML and GPU-accelerated XGBoost 3. Selection of appropriate algorithms for GPU execution
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. Groupby, apply, and aggregation operations 2. cuDF vs pandas API mapping and usage 3. Data integration, joining, merging, and filtering
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. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q139-Q144):

NEW QUESTION # 139
You are training a deep learning model on a large dataset of images stored in an Amazon S3 bucket.
You want to optimize data loading, augmentation, and preprocessing on NVIDIA GPUs to avoid CPU bottlenecks.
Which of the following approaches is the most efficient for GPU-accelerated data preprocessing?

A. Load the dataset using PyTorch's torchvision.transforms and DataLoader, leveraging the CPU for data preprocessing and transferring batches to the GPU before training.
B. Use NVIDIA DALI to decode images, apply transformations such as resizing and normalization, and load batches directly to the GPU for training.
C. Use OpenCV to load and preprocess images on the CPU, then transfer the processed images to the GPU before training.
D. Use TensorFlow's tf.data API with tf.image transformations and ensure that the preprocessed images are transferred to GPU memory at the end of the pipeline.

Answer: B

NEW QUESTION # 140
A data scientist is working on a social network analysis project where they need to find the most influential users in a large-scale graph dataset. The dataset consists of millions of users connected through directed edges.
Which of the following approaches would be the best choice for this task using NVIDIA GPU-accelerated tools?

A. Convert the graph into a pandas DataFrame and apply NetworkX's PageRank implementation.
B. Use cuGraph's pagerank() function to identify influential nodes based on link structure.
C. Use cuDF's groupby().sum() function to count the number of connections per user.
D. Use cuGraph's bfs() (Breadth-First Search) to find the most influential nodes.

Answer: B

NEW QUESTION # 141
You are tasked with optimizing a data science workflow to scale across multiple GPUs using Dask.
Which of the following approaches would be most effective for implementing data parallelism in this scenario? (Select two)

A. Manually partition the dataset and assign each partition to a specific GPU using dask.delayed
B. Use Dask's default scheduler to distribute work across GPUs without any specific configuration
C. Use Dask's distributed scheduler along with dask_cuda to handle GPU resources for parallel processing
D. Use dask_cuda's CUDACluster to manage the multi-GPU setup and parallelize computations
E. Run the computation on a single GPU and let Dask handle CPU parallelism automatically

Answer: C,D

NEW QUESTION # 142
A data scientist is working with a large dataset containing millions of records and aims to accelerate the data preprocessing workflow using NVIDIA technologies.
Which of the following approaches is the most effective for optimizing data preprocessing performance using GPUs?

A. Using Dask to distribute processing across multiple CPU cores
B. Using NumPy for array computations and parallelizing with Python's multiprocessing
C. Using RAPIDS cuDF to replace pandas operations
D. Running data transformations in a multi-threaded CPU environment

Answer: C

NEW QUESTION # 143
You are working on a large dataset for a machine learning model and need to preprocess the data efficiently using NVIDIA RAPIDS cuDF on a GPU-accelerated system.
Which of the following statements is correct regarding data preparation using cuDF?

A. cuDF supports common Pandas-like operations, such as filtering, merging, and groupby, but performs them efficiently on the GPU.
B. cuDF can be used as a direct replacement for Pandas, but it is optimized for multi-node distributed computing rather than GPU acceleration.
C. cuDF requires all input data to be stored in Apache Parquet format, as it does not support CSV or JSON ingestion.
D. cuDF operations are executed lazily and require an explicit compute() call to execute transformations.

Answer: A

NEW QUESTION # 144
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