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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q149-Q154):

NEW QUESTION # 149
You are working with a large-scale social network dataset and need to analyze relationships between users to detect communities using the Louvain algorithm. Given the benefits of GPU acceleration, you decide to use cuGraph for this task.
Which of the following statements best describes why cuGraph is beneficial for this workload?

A. Running graph algorithms on a GPU leads to higher latency due to the overhead of data movement between CPU and GPU.
B. cuGraph primarily accelerates deep learning tasks and is not optimized for graph-based algorithms.
C. cuGraph enables parallel computation on the GPU, significantly speeding up community detection tasks compared to traditional CPU-based implementations.
D. cuGraph does not support weighted graphs, making it less useful for real-world social network analysis.

Answer: C

NEW QUESTION # 150
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 choice of GPU instance does not affect cuGraph performance since all GPUs execute graph algorithms at the same speed.
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 availability of NVIDIA CUDA-enabled GPUs, as cuGraph requires CUDA for acceleration.

Answer: D

NEW QUESTION # 151
You are comparing the performance of NVIDIA RAPIDS cuML, TensorFlow, and PyTorch for training and inference on a dataset with millions of records.
To design a fair and effective benchmark, which approach should you take?

A. Ensure all frameworks run on the same GPU, use optimized batch sizes, and measure execution time and memory usage with NVIDIA Nsight Systems.
B. Run each framework on different GPUs to maximize available resources and compare execution times across different hardware configurations.
C. Use only a CPU baseline for comparison to demonstrate the benefits of GPU acceleration, ignoring GPU-specific optimizations.

Answer: A

NEW QUESTION # 152
A financial analyst wants to create an interactive GPU-accelerated dashboard to visualize stock price movements in real-time.
Which NVIDIA-supported tool is best suited for this purpose?

A. Rely on Matplotlib to generate static plots and update them every minute with a loop.
B. Convert the stock price dataset into a NumPy array and visualize it using Seaborn's line plot.
C. Use Plotly Dash with RAPIDS cuDF to create an interactive GPU-powered dashboard.
D. Precompute the time-series visualization with Dask and display it in a static HTML page.

Answer: C

NEW QUESTION # 153
A data scientist is working with an imbalanced dataset in a fraud detection project. The dataset contains 1 million transactions, but only 2% of them are labeled as fraudulent. To improve the performance of the model, the scientist decides to generate synthetic data using NVIDIA RAPIDS cuDF.
Which of the following approaches is the best way to generate synthetic samples while preserving data characteristics?

A. Use cudf.DataFrame.interpolate(method='linear') to create new fraudulent samples by interpolating between existing ones.
B. Apply cuML.SMOTE() to generate synthetic samples based on the minority class distribution.
C. Use cudf.DataFrame.append(cudf.DataFrame.random()) to create new fraudulent transactions.
D. Use cudf.DataFrame.sample(frac=0.5, replace=True) to oversample the minority class.

Answer: B

NEW QUESTION # 154
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