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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. CRISP-DM workflow execution 2. Benchmarking GPU workflows 3. Docker and Conda environment management
Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Data caching and performance optimization 2. Distributed data processing frameworks (Dask) 3. GPU-accelerated ETL design and implementation
Machine Learning	15%	- Model Development and Optimization 1. Multi-GPU training comparison 2. Hyperparameter tuning 3. Memory optimization techniques (mixed precision, batching) 4. Feature engineering
MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Model deployment in production environments 3. Performance benchmarking and optimization
Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. Synthetic data generation with RAPIDS 3. cuDF and pandas data preprocessing
Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Perform time series analysis and visualization 2. Use cuGraph for graph analytics 3. Detect anomalies in time series datasets

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q224-Q229):

NEW QUESTION # 224
Which of the following steps is the first in the CRISP-DM (Cross-Industry Standard Process for Data Mining) process when using NVIDIA technologies?

A. Model Building
B. Data Preparation
C. Business Understanding
D. Data Understanding

Answer: C

NEW QUESTION # 225
A data scientist is analyzing a large time-series dataset containing stock price movements of thousands of companies over a decade. The dataset is stored as a cuDF DataFrame and contains millions of rows. The scientist wants to visualize trends and patterns interactively while leveraging GPU acceleration.
Which of the following approaches is the most efficient for visualizing this time-series data?

A. Convert the cuDF DataFrame to Pandas and use matplotlib for plotting.
B. Use seaborn with a sampled subset of the dataset to generate line plots.
C. Use cuXfilter with a cuDF DataFrame to generate interactive visualizations directly on the GPU.
D. Use matplotlib with plt.plot() while applying df.to_pandas() to convert data.

Answer: C

NEW QUESTION # 226
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 RAPIDS cuML to accelerate random forest training using GPU-optimized implementations.
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 TensorRT to accelerate random forest model training by optimizing tree-based algorithms.

Answer: A

NEW QUESTION # 227
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. Use cudf.DataFrame.append(cudf.DataFrame.random()) to create new fraudulent transactions.
C. Apply cuML.SMOTE() to generate synthetic samples based on the minority class distribution.
D. Use cudf.DataFrame.sample(frac=0.5, replace=True) to oversample the minority class.

Answer: C

NEW QUESTION # 228
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. Convert time-series data to tensors and train a deep learning model using PyTorch with mixed precision on an NVIDIA GPU.
B. Use RAPIDS cuDF and Dask-cuDF to preprocess the data and train an LSTM model with PyTorch Lightning on multiple GPUs.
C. Use RAPIDS cuML's XGBoost-GPU implementation to forecast time-series patterns with improved training efficiency.
D. Run Facebook Prophet on a multi-core CPU environment to leverage parallel forecasting.

Answer: B

NEW QUESTION # 229
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