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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q104-Q109):

NEW QUESTION # 104
You are working with a large dataset containing numeric and categorical features, which will be processed using NVIDIA RAPIDS cuDF for accelerated analytics.
To optimize performance while minimizing memory usage, which data type is the most appropriate for storing a categorical variable with a small number of unique values?

A. float32 - Reduces memory consumption compared to float64 while maintaining precision.
B. bool - Minimizes memory usage and supports efficient operations for categorical data.
C. category - Optimizes storage and computation for categorical data in cuDF.
D. int64 - Provides high precision and avoids potential overflow.

Answer: C

NEW QUESTION # 105
A data engineering team is tasked with processing terabytes of log data every hour using an ETL pipeline. Due to the large data volume, they need a scalable GPU-accelerated solution that can distribute data processing across multiple GPUs.
Which approach best meets their needs?

A. Use cuDF alone for processing log data, as it provides optimal performance on a single GPU.
B. Process data using Pandas, then export the results to a CSV file for GPU-accelerated analytics.
C. Use Dask-cuDF to distribute cuDF DataFrame operations across multiple GPUs, enabling parallel ETL processing.
D. Use NumPy for data transformations before converting the dataset into cuDF for final storage.

Answer: C

NEW QUESTION # 106
You are a data scientist analyzing a social media network with NVIDIA cuGraph to identify the most influential users using the PageRank algorithm.
Which option best describes how cuGraph PageRank operates on a directed graph?

A. PageRank in cuGraph operates only on undirected graphs and cannot be applied to networks where edges have a direction.
B. PageRank assigns equal importance to all nodes in the graph initially and updates values only based on outgoing edges, ignoring incoming edges.
C. PageRank in cuGraph is a label propagation algorithm that clusters nodes into communities rather than ranking their importance.
D. PageRank in cuGraph uses an iterative power method to update node importance values based on incoming edges, incorporating a damping factor to handle random jumps.

Answer: D

NEW QUESTION # 107
A data scientist is using NVIDIA RAPIDS to perform statistical analysis as part of exploratory data analysis (EDA) on a dataset containing millions of product reviews. They need to compute basic descriptive statistics such as mean, median, and variance efficiently.
Which of the following methods is the most appropriate for performing these calculations on GPUs?

A. Use a traditional SQL database to compute statistics and then transfer results to the GPU
B. Use NumPy's statistical functions, such as numpy.mean() and numpy.var()
C. Convert the dataset into a PyTorch tensor and use PyTorch's statistical methods
D. Use cuDF's built-in statistical functions like .mean(), .median(), and .var()

Answer: D

NEW QUESTION # 108
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 DALI to perform preprocessing and cache the output before the shuffle operation, thereby eliminating the need for shuffle.
B. Cache the data in GPU memory using RAPIDS cuDF for faster access, and leverage GPU-based partitioning to reduce shuffle size.
C. Use RAPIDS cuDF to cache the shuffled data on disk and then re-load it from disk during subsequent stages.
D. Implement Spark's default disk caching to store shuffle results, allowing the GPU to access disk data directly.

Answer: C

NEW QUESTION # 109
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