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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- Feature engineering and data type optimization - Data validation and quality assurance - Workflow monitoring and bottleneck identification - Data cleaning, preprocessing and transformation
Topic 2: Data Analysis	14%	- Exploratory Data Analysis (EDA) - Data visualization and graph analytics - Distributed and parallel data processing - Time-series analysis and anomaly detection
Topic 3: Machine Learning	15%	- Model evaluation and validation - Distributed training strategies - Model training and hyperparameter tuning - GPU-accelerated ML frameworks and algorithms
Topic 4: GPU and Cloud Computing	16%	- GPU architecture and acceleration principles - Cloud GPU environments and deployment - CRISP-DM and data science methodology - Resource management and scaling strategies
Topic 5: Data Manipulation and Software Literacy	19%	- GPU-accelerated ETL workflows - Data processing libraries selection and usage - Performance profiling and optimization tools - Dependency management and containerization
Topic 6: MLOps	19%	- Monitoring, logging and maintenance - Model deployment and serving - End-to-end workflow management - Pipeline automation and orchestration

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

NEW QUESTION # 12
A data scientist is working with a large dataset for a machine learning model and wants to accelerate feature engineering using a GPU.
Which of the following approaches will provide the most significant performance boost when using GPU acceleration?

A. Using traditional pandas DataFrames and NumPy operations optimized for CPU processing.
B. Using a single-threaded feature extraction approach to avoid overhead from parallelization.
C. Reducing dataset size by randomly removing data points without considering class balance.
D. Using RAPIDS cuDF and cuML libraries to perform feature transformations on a GPU.

Answer: D

NEW QUESTION # 13
You are tasked with designing and implementing a benchmark to compare the performance of different deep learning frameworks, including TensorFlow, PyTorch, and JAX, using NVIDIA GPUs.
Which of the following is the most effective approach to ensure an accurate and fair comparison?

A. Use mixed precision (FP16) training only in TensorFlow to maximize performance while keeping other frameworks at FP32.
B. Run each framework with default settings to compare their out-of-the-box performance without any optimizations.
C. Ensure identical hardware configurations, dataset preprocessing, and model architectures while leveraging NVIDIA's Nsight Systems and DLProf for analysis.
D. Compare training times only without considering throughput, power efficiency, or memory utilization.

Answer: C

NEW QUESTION # 14
Which of the following is the best approach for performing benchmarking and optimizing GPU- accelerated workflows for MLOps using Nvidia technologies?

A. Use Nvidia's nvprof tool to profile GPU resource usage and identify bottlenecks, then adjust the batch size to optimize throughput.
B. Rely exclusively on the nvidia-smi tool for monitoring GPU utilization and memory usage across multiple GPUs without making any other performance adjustments.
C. Use Nvidia's nsight tools to benchmark only the model training phase and ignore the inference phase, as training is the primary bottleneck.
D. Use TensorRT to optimize deep learning models by converting them into highly optimized inference engines, allowing faster execution with lower latency.

Answer: A

NEW QUESTION # 15
You are working on a large-scale graph analysis project using NVIDIA cuGraph for accelerated computations. Your dataset consists of millions of nodes and edges representing social network interactions. You need to efficiently compute PageRank while minimizing memory usage.
Which of the following techniques would be the most effective?

A. Convert the graph into an adjacency list format and store it in Python dictionaries for faster lookups.
B. Use cuGraph's sparse matrix representation to store graph data and perform computations efficiently.
C. Manually unroll loops and precompute node ranks in a NumPy array before feeding them into cuGraph.
D. Use Pandas DataFrames to manage the graph structure and leverage Pandas indexing for efficient queries.

Answer: B

NEW QUESTION # 16
You have a massive time-series dataset containing millions of records per day, and you need to perform forecasting at scale.
Which of the following techniques best utilizes NVIDIA technologies to optimize time-series forecasting?

A. Run Facebook Prophet on a multi-core CPU environment to leverage parallel forecasting.
B. Use RAPIDS cuML's XGBoost-GPU implementation to forecast time-series patterns with improved training efficiency.
C. Use RAPIDS cuDF and Dask-cuDF to preprocess the data and train an LSTM model with PyTorch Lightning on multiple GPUs.
D. Convert time-series data to tensors and train a deep learning model using PyTorch with mixed precision on an NVIDIA GPU.

Answer: C

NEW QUESTION # 17
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