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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Machine Learning	15%	- Model Development and Optimization 1. Hyperparameter tuning 2. Memory optimization techniques (mixed precision, batching) 3. Feature engineering 4. Multi-GPU training comparison
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
MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Model deployment in production environments 3. Performance benchmarking and optimization
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
GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. CRISP-DM workflow execution 3. Benchmarking GPU workflows
Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. Synthetic data generation with RAPIDS 3. cuDF and pandas data preprocessing

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q97-Q102):

NEW QUESTION # 97
You are working with a large time-series dataset consisting of millions of records and want to efficiently visualize trends over time using NVIDIA technologies. The dataset is stored as a cuDF DataFrame, and you need to generate an interactive line plot with minimal performance overhead.
Which of the following is the best approach to achieve this goal?

A. Convert the cuDF DataFrame to a Pandas DataFrame and plot using Matplotlib
B. Load the data into a Spark DataFrame and visualize using Apache Zeppelin
C. Use the Bokeh library to plot the time-series data from a cuDF DataFrame directly
D. Use the hvPlot library with RAPIDS cuDF to directly render the time-series data interactively

Answer: D

NEW QUESTION # 98
You are tasked with implementing data caching to reduce shuffle in an accelerated machine learning pipeline using NVIDIA technologies. You need to cache intermediate results after a shuffle operation in a distributed setting.
Which of the following is the best approach to minimize shuffle overhead and maximize performance?

A. Use RAPIDS cuDF to cache the shuffled data on disk and then re-load it from disk during subsequent stages.
B. Use DALI to perform preprocessing and cache the output before the shuffle operation, thereby eliminating the need for shuffle.
C. Implement Spark's default disk caching to store shuffle results, allowing the GPU to access disk data directly.
D. Cache the data in GPU memory using RAPIDS cuDF for faster access, and leverage GPU-based partitioning to reduce shuffle size.

Answer: A

NEW QUESTION # 99
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 TensorRT to accelerate random forest model training by optimizing tree-based algorithms.
B. NVIDIA DeepStream to preprocess tabular data and optimize random forest model execution.
C. NVIDIA RAPIDS cuML to accelerate random forest training using GPU-optimized implementations.
D. NVIDIA Triton Inference Server to distribute random forest model training across multiple GPUs.

Answer: C

NEW QUESTION # 100
You are working on a machine learning problem that involves training a deep learning model on a dataset with billions of records. The dataset is stored in a distributed cloud storage system.
Given the need for acceleration, which is the most effective approach?

A. Store the dataset in a relational database and query it sequentially using SQL before training the model.
B. Use GPU acceleration with libraries like RAPIDS AI or TensorFlow to leverage parallel processing.
C. Load the entire dataset into RAM on a single powerful CPU-based machine before starting model training.
D. Reduce the dataset to a small representative sample to avoid the need for specialized acceleration.

Answer: B

NEW QUESTION # 101
You are working on an MLOps pipeline with a large dataset using NVIDIA technologies. You need to assess the memory size of the dataset before training the model to avoid memory overflow errors.
Which of the following is the best method for verifying the memory size of the dataset on the GPU using NVIDIA tools?

A. Use cuDF to load the dataset into GPU memory and use the nvidia-smi tool to monitor GPU memory usage during dataset loading.
B. Use PyTorch's torch.cuda.memory_allocated() function to check the memory allocated on the GPU after the dataset is loaded.
C. Use NVIDIA DALI to process the dataset and directly query the memory size through DALI's DataLoader function.
D. Load the dataset into a CPU-based pandas dataframe and use the pandas.memory_usage() function to estimate the dataset's size before transferring it to the GPU.

Answer: A

NEW QUESTION # 102
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