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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation	17%	- Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - 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 - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables
Data Analysis	14%	- Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Visualization 1. Selecting appropriate plots for different analysis goals 2. Visualizing data using Plotly and Matplotlib
Data Manipulation and Software Literacy	19%	- Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency - GPU-accelerated data manipulation using cuDF 1. Groupby, apply, and aggregation operations 2. Data integration, joining, merging, and filtering 3. cuDF vs pandas API mapping and usage - Distributed computing with Dask 1. Dask-cuDF for parallel data processing 2. Scaling data operations across multiple GPUs
Machine Learning	15%	- Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch - 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. Training models using cuML and GPU-accelerated XGBoost 2. Selection of appropriate algorithms for GPU execution 3. Multi-GPU training strategies
GPU and Cloud Computing	16%	- GPU resource management 1. Efficient GPU resource allocation and scheduling - Performance optimization 1. Memory profiling with DLProf 2. Single and multi-GPU performance optimization 3. Mixed precision and bottleneck analysis - 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
MLOps	19%	- Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - 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 - Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q16-Q21):

NEW QUESTION # 16
You are training a large-scale random forest model on a dataset with millions of rows and hundreds of features. The training time is significantly high when using traditional CPU-based machine learning frameworks.
Which NVIDIA technology should you use to accelerate training while maintaining compatibility with common ML frameworks like scikit-learn?

A. NVIDIA TensorRT to accelerate random forest model training by optimizing tree-based algorithms.
B. NVIDIA Triton Inference Server to distribute random forest model training across multiple GPUs.
C. NVIDIA DeepStream to preprocess tabular data and optimize random forest model execution.
D. NVIDIA RAPIDS cuML to accelerate random forest training using GPU-optimized implementations.

Answer: D

NEW QUESTION # 17
A financial services company is deploying an AI-driven risk assessment model using NVIDIA GPUs on a cloud platform. To optimize resource utilization and cost efficiency, they need to determine the best GPU deployment strategy.
Which of the following is the most effective approach?

A. Deploy the model using NVIDIAAI Enterprise with MIG (Multi-Instance GPU) to allocate multiple workloads on a single GPU efficiently.
B. Run all AI workloads on a single large GPU instance without any partitioning or workload separation.
C. Choose an on-demand cloud instance with an outdated GPU model to reduce costs, even if performance is compromised.
D. Deploy separate full-GPU instances for each workload, even if they have variable compute demands.

Answer: A

NEW QUESTION # 18
Which of the following is the most efficient method for processing big data in a distributed environment using NVIDIA technologies?

A. Utilizing a distributed data processing framework like Apache Spark with GPU acceleration from the NVIDIA RAPIDS library.
B. Using cloud-based, non-GPU infrastructure for data processing
C. Using a single-node GPU-based solution for data processing.
D. Relying on traditional CPU-only processing frameworks for big data tasks.

Answer: A

NEW QUESTION # 19
You are working with a large dataset on an NVIDIA GPU, where optimizing memory usage is a priority. Your dataset contains a column, transaction_id, which stores unique integer values ranging between 0 and 100,000.
Which of the following data types is the most memory-efficient choice for this column in cuDF?

A. df['transaction_id'] = df['transaction_id'].astype('int64')
B. df['transaction_id'] = df['transaction_id'].astype('int32')
C. df['transaction_id'] = df['transaction_id'].astype('int8')
D. df['transaction_id'] = df['transaction_id'].astype('float32')

Answer: B

NEW QUESTION # 20
A data science team wants to deploy a GPU-accelerated pipeline using cuGraph to analyze graph data on cloud infrastructure. They are evaluating different cloud-based GPU solutions.
Which of the following factors should they consider when selecting a cloud-based GPU instance for running cuGraph efficiently?

A. The availability of NVIDIA CUDA-enabled GPUs, as cuGraph requires CUDA for acceleration.
B. cuGraph runs equally well on CPU-based virtual machines, making GPU instances unnecessary.
C. Cloud-based GPUs are only useful for rendering graphics, not for running cuGraph algorithms.
D. The choice of GPU instance does not affect cuGraph performance since all GPUs execute graph algorithms at the same speed.

Answer: A

NEW QUESTION # 21
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