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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q64-Q69):

NEW QUESTION # 64
You are preparing a dataset for training a machine learning model using NVIDIA RAPIDS cuML. The dataset contains a feature representing timestamps in nanoseconds.
To optimize GPU performance while ensuring precision, which data type should you choose?

A. datetime64[ns] - Optimizes storage and computation for timestamp data in RAPIDS.
B. int32 - Uses less memory and can store high-precision timestamps efficiently.
C. object - Allows flexibility in storing timestamps as strings for easier parsing.
D. bool - Provides a highly efficient way to store timestamps as binary values.

Answer: A

NEW QUESTION # 65
You are working with a large dataset containing 500 million records stored as a parquet file. Your task is to perform data filtering, aggregation, and transformation as efficiently as possible.
Which of the following approaches would be the best choice for accelerated data manipulation using NVIDIA technologies?

A. Use RAPIDS cuDF to load, filter, and transform the dataset on the GPU
B. Convert the dataset to a NumPy array and use standard Python loops
C. Load the dataset into pandas and use apply() for transformations
D. Use multiprocessing in Python to parallelize pandas operations across CPU cores

Answer: A

NEW QUESTION # 66
You are developing an end-to-end data pipeline that processes terabytes of image metadata using NVIDIA technologies. You need a software stack that efficiently integrates GPU-accelerated data processing, machine learning, and visualization.
Which of the following tool combinations is best suited for this task?

A. cuDF for data manipulation, cuML for machine learning, and Plotly for visualization.
B. Pandas for data manipulation, XGBoost for machine learning, and Matplotlib for visualization.
C. Hadoop for data storage, NumPy for computations, and TensorFlow for visualization.
D. Excel for data analysis, scikit-learn for machine learning, and Seaborn for visualization.

Answer: A

NEW QUESTION # 67
You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?

A. Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration.
B. Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.
C. Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.
D. Use a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values.

Answer: A

NEW QUESTION # 68
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. Use seaborn with a sampled subset of the dataset to generate line plots.
B. Convert the cuDF DataFrame to Pandas and use matplotlib for plotting.
C. Use matplotlib with plt.plot() while applying df.to_pandas() to convert data.
D. Use cuXfilter with a cuDF DataFrame to generate interactive visualizations directly on the GPU.

Answer: D

NEW QUESTION # 69
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