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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Analysis	14%	- Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - 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
Data Preparation	17%	- Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables - GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet - Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance - Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading
MLOps	19%	- Model deployment and serving 1. Production deployment strategies 2. Model saving, loading, and prediction generation - Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools - Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows - Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility
Machine Learning	15%	- Model training with GPU acceleration 1. Selection of appropriate algorithms for GPU execution 2. Multi-GPU training strategies 3. Training models using cuML and GPU-accelerated XGBoost - Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch - Feature engineering and hyperparameter tuning 1. Hyperparameter tuning techniques 2. Batching and memory-efficient training methods 3. Feature engineering for ML models
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. 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
GPU and Cloud Computing	16%	- 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 - GPU resource management 1. Efficient GPU resource allocation and scheduling - Performance optimization 1. Memory profiling with DLProf 2. Mixed precision and bottleneck analysis 3. Single and multi-GPU performance optimization

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q12-Q17):

NEW QUESTION # 12
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. Enable CPU offloading to balance the workload between the CPU and GPU.
B. Increase the batch size significantly to improve GPU utilization.
C. Use mixed-precision inference with TensorRT to accelerate computation.
D. Reduce the model size by randomly pruning neurons without retraining.

Answer: C

NEW QUESTION # 13
A research team is analyzing large-scale social interactions and wants to identify strongly connected communities within a massive graph dataset using NVIDIA's cuGraph library.
Which method would be the most efficient for this task?

A. Apply cuGraph's Label Propagation Algorithm (LPA) to divide the graph into communities without predefining the number of clusters.
B. Run the cuGraph PageRank algorithm and classify nodes with high scores as community leaders.
C. Apply cuGraph's Dijkstra's algorithm to find the shortest paths between all nodes and group them into communities.
D. Use cuGraph's Louvain method to detect hierarchical communities based on modularity optimization.

Answer: D

NEW QUESTION # 14
You are working on a data science project that involves processing large-scale financial transaction data. You want to optimize data manipulation operations using NVIDIA's RAPIDS cuDF.
Which of the following approaches best leverages NVIDIA technologies for efficient data manipulation?

A. Load the dataset into a Pandas DataFrame and use multi-threading to speed up operations.
B. Use cuDF DataFrames for data manipulation and rely on GPU-accelerated functions like .groupby(),
.merge(), and .applymap().
C. Convert the dataset into a SQLite database and execute SQL queries to perform data transformations.
D. Preprocess the data using Apache Spark's CPU-based DataFrame API before transferring it to a GPU for machine learning.

Answer: B

NEW QUESTION # 15
You are using RAPIDS cuML to train a regression model on a dataset with features of varying scales (temperature in Celsius, revenue in thousands, customer age). To improve model performance, you decide to standardize the data.
Which approach correctly standardizes the data using NVIDIA technologies?

A. Use numpy.mean() and numpy.std() to manually standardize the dataset before feeding it into the GPU.
B. Use cuml.MinMaxScaler() to scale the features to a range of [0,1] without adjusting for mean and variance.
C. Use cuml.PCA() to reduce the dimensionality of the dataset, which also standardizes feature variance.
D. Use cuml.StandardScaler() to transform the features to have a mean of zero and a standard deviation of one.

Answer: D

NEW QUESTION # 16
A machine learning engineer is working on a multi-GPU workload using Dask and RAPIDS to process a massive dataset efficiently. However, they notice that GPU utilization is not optimal, and data transfer between GPUs is slowing down computation.
What is the best approach to minimize data transfer overhead and maximize parallel efficiency?

A. Use NVLink to enable high-bandwidth inter-GPU communication and dask_cuda.LocalCUDACluster
B. Transfer data between GPUs using NumPy's .copy() function
C. Use Dask's single-threaded scheduler to avoid multi-threading overhead
D. Load the dataset into a Pandas DataFrame and distribute computations across GPUs manually

Answer: A

NEW QUESTION # 17
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