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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	14%	- Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting - Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals
Topic 2: 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 - Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud - GPU architecture and fundamentals 1. GPU architecture fundamentals for data science 2. CPU vs GPU workloads and memory transfer optimization - GPU resource management 1. Efficient GPU resource allocation and scheduling
Topic 3: Data Preparation	17%	- 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 - Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables
Topic 4: Machine Learning	15%	- Model training with GPU acceleration 1. Selection of appropriate algorithms for GPU execution 2. Training models using cuML and GPU-accelerated XGBoost 3. Multi-GPU training strategies - Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts - Feature engineering and hyperparameter tuning 1. Feature engineering for ML models 2. Hyperparameter tuning techniques 3. Batching and memory-efficient training methods
Topic 5: MLOps	19%	- Experiment tracking 1. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware - 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. Model saving, loading, and prediction generation 2. Production deployment strategies - Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows
Topic 6: Data Manipulation and Software Literacy	19%	- Distributed computing with Dask 1. Dask-cuDF for parallel data processing 2. Scaling data operations across multiple GPUs - 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 - Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q232-Q237):

NEW QUESTION # 232
You are working on a large-scale graph analysis problem that involves computing the shortest paths between nodes in a massive social network dataset. You decide to leverage NVIDIA RAPIDS cuGraph for accelerated computation.
Which of the following cuGraph functions should you use?

A. cugraph.pagerank()
B. cugraph.sssp()
C. cugraph.label_propagation()
D. cugraph.k_truss()

Answer: B

NEW QUESTION # 233
You are working with a dataset consisting of 100 million records stored in a distributed system. The dataset includes numerical and categorical variables, requiring both exploratory data analysis (EDA) and machine learning model training. The processing time using traditional CPU-based methods is too slow.
Which of the following techniques would be the most effective acceleration method to handle this workload efficiently?

A. Scale up to a high-core-count CPU machine
B. Store the dataset in a relational database and query it using SQL
C. Use RAPIDS cuDF for GPU-accelerated data processing
D. Reduce the dataset to a smaller sample size before processin

Answer: C

NEW QUESTION # 234
A data science team is deploying a deep learning model for real-time inference. The model is optimized for inference on an NVIDIA A100 GPU, but the team notices that inference latency is higher than expected.
Which of the following optimizations is most effective in reducing inference latency?

A. Use mixed-precision inference with TensorRT to accelerate computation.
B. Reduce the model size by randomly pruning neurons without retraining.
C. Increase the batch size significantly to improve GPU utilization.
D. Enable CPU offloading to balance the workload between the CPU and GPU.

Answer: A

NEW QUESTION # 235
A team of data scientists needs to deploy a machine learning model that depends on specific versions of CUDA and TensorFlow, ensuring it runs consistently across different machines without manually configuring each system.
Which of the following approaches best ensures consistency while leveraging NVIDIA GPUs?

A. Compiling all dependencies into the host machine and using system-wide installations
B. Running the model in a local Python virtual environment and copying dependencies manually
C. Using NVIDIA Docker (nvidia-docker) to containerize the model and manage GPU dependencies
D. Using Docker without GPU support and relying on CPU fallback when running TensorFlow

Answer: C

NEW QUESTION # 236
You are working on a data science project where you need to process a large dataset containing
500 million records. You want to determine whether GPU acceleration would significantly improve performance.
Which of the following factors best indicates that you should use an accelerated computing solution like RAPIDS?

A. The dataset consists of simple arithmetic operations on a few columns and can be processed using vectorized NumPy operations.
B. The dataset is a structured table with less than 100,000 records and can be handled efficiently with a Pandas DataFrame.
C. The dataset has high-dimensional sparse features and requires complex operations such as nearest neighbor search and clustering.
D. The dataset is heavily structured but mainly requires text-based analysis using regex-based search and manipulation.

Answer: C

NEW QUESTION # 237
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Candidates who become NVIDIA NCP-ADS certified demonstrate their worth in the NVIDIA field. The NVIDIA-Certified-Professional Accelerated Data Science (NCP-ADS) certification is proof of their competence and skills. This is a highly sought-after skill in large NVIDIA companies and makes a career easier for the candidate. To become certified, you must pass the NVIDIA-Certified-Professional Accelerated Data Science (NCP-ADS) certification exam. For this task, you need high-quality and accurate NVIDIA-Certified-Professional Accelerated Data Science (NCP-ADS) exam dumps.

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