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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
GPU and Cloud Computing	16%	- Performance optimization 1. Memory profiling with DLProf 2. Mixed precision and bottleneck analysis 3. Single and multi-GPU performance optimization - 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
Data Manipulation and Software Literacy	19%	- Distributed computing with Dask 1. Scaling data operations across multiple GPUs 2. Dask-cuDF for parallel data processing - 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. Groupby, apply, and aggregation operations 2. cuDF vs pandas API mapping and usage 3. Data integration, joining, merging, and filtering
MLOps	19%	- Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management - Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - Experiment tracking 1. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware - Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies
Data Preparation	17%	- Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables - 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. NVIDIA DALI for high-performance data loading 2. Handling class imbalance and generating synthetic data
Machine Learning	15%	- Feature engineering and hyperparameter tuning 1. Batching and memory-efficient training methods 2. Hyperparameter tuning techniques 3. Feature engineering for ML models - 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
Data Analysis	14%	- Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis - Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q113-Q118):

NEW QUESTION # 113
A data engineer is preparing a dataset for training a deep learning model. The dataset contains numerical features with missing values, outliers, and inconsistent units.
Which of the following strategies is the most appropriate for ensuring a standardized and clean dataset?

A. Remove all rows with missing values and outliers to ensure only clean data is used.
B. Replace missing values with the mean, apply z-score normalization, and clip extreme outliers based on a threshold (e.g., 3 standard deviations).
C. Use the median to fill missing values, convert all numerical values into categorical bins, and apply Min-Max scaling.
D. Standardize the dataset using the mean and standard deviation, but keep missing values and outliers unchanged to avoid data manipulation.

Answer: B

NEW QUESTION # 114
You are training a machine learning model using scikit-learn-like API on a dataset with millions of samples and thousands of features. You need to optimize both training time and inference speed using NVIDIA technologies.
Which solution is the most appropriate?

A. Use NVIDIA RAPIDS cuML for GPU-accelerated machine learning model training.
B. Use NVIDIA Magnum IO to optimize machine learning model parameters on the GPU.
C. Use NVIDIA Modulus to accelerate machine learning training and feature selection.
D. Use NVIDIA Triton Inference Server to train the model efficiently on a single GPU.

Answer: A

NEW QUESTION # 115
Which of the following best describes the role of MLOps in the context of NVIDIA technologies for deploying machine learning models in production? (Select two)

A. MLOps helps manage the lifecycle of machine learning models, ensuring efficient collaboration and model governance
B. MLOps ensures that models trained on GPUs can only run on GPUs during deployment
C. MLOps frameworks support version control and automation, ensuring reproducibility and scalability of ML workflows
D. MLOps replaces the need for data preprocessing during training and deployment

Answer: A,C

NEW QUESTION # 116
A data scientist is working with large-scale tabular datasets and wants to optimize data ingestion and storage for accelerated processing on NVIDIA GPUs. The scientist is considering different file formats and storage optimizations to maximize performance in a RAPIDS-based workflow.
Which of the following approaches is the most suitable for optimizing both storage and processing performance?

A. Load data directly into NumPy arrays before using RAPIDS cuDF for processing.
B. Use JSON format for easy readability and process it directly in cuDF.
C. Store data in Parquet format and load it using cuDF in RAPIDS.
D. Convert datasets into CSV format and store them in local disk storage.

Answer: C

NEW QUESTION # 117
You are processing a large dataset using RAPIDS cuDF and Dask-cuDF on an NVIDIA GPU. Your profiling indicates that data transfer times between CPU and GPU are significantly slowing down your pipeline.
What is the most effective way to reduce this bottleneck?

A. Convert the dataset into a CSV format before transferring it to the GPU
B. Increase the CPU RAM allocation to store more data before transferring to the GPU
C. Transfer data in multiple smaller chunks to the GPU instead of larger batches
D. Use cudf.read_parquet() instead of Pandas to load data directly into GPU memory

Answer: D

NEW QUESTION # 118
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