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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
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
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
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
Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. Synthetic data generation with RAPIDS 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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q168-Q173):

NEW QUESTION # 168
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. Load the entire dataset into RAM on a single powerful CPU-based machine before starting model training.
C. Use GPU acceleration with libraries like RAPIDS AI or TensorFlow to leverage parallel processing.
D. Reduce the dataset to a small representative sample to avoid the need for specialized acceleration.

Answer: C

NEW QUESTION # 169
In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

A. Better for memory-intensive workloads
B. Lower energy consumption compared to CPUs
C. Efficient handling of matrix operations in machine learning models
D. Faster parallel processing for large datasets
E. Lower cost of cloud infrastructure

Answer: C,D

NEW QUESTION # 170
You are working on a data science project where you need to process a large dataset containing
500 million records. You want to determine whether GPU acceleration would significantly improve performance.
Which of the following factors best indicates that you should use an accelerated computing solution like RAPIDS?

A. The dataset is a structured table with less than 100,000 records and can be handled efficiently with a Pandas DataFrame.
B. The dataset is heavily structured but mainly requires text-based analysis using regex-based search and manipulation.
C. The dataset consists of simple arithmetic operations on a few columns and can be processed using vectorized NumPy operations.
D. The dataset has high-dimensional sparse features and requires complex operations such as nearest neighbor search and clustering.

Answer: D

NEW QUESTION # 171
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 a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values.
B. Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.
C. Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.
D. Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration.

Answer: D

NEW QUESTION # 172
You are analyzing a transportation network where airports represent nodes and flight routes represent edges. You need to determine the most critical airports in the network based on how many shortest paths pass through them.
Which cuGraph centrality algorithm should you use for this task?

A. Betweenness Centrality, as it identifies airports that act as major transit hubs by measuring how frequently they appear in shortest paths.
B. Closeness Centrality, as it measures how close an airport is to all other airports, making it the best indicator of global connectivity.
C. Degree Centrality, as it measures the number of direct connections an airport has, indicating its importance in the network.
D. Triangle Count, as it determines the number of three-node cycles an airport is part of, indicating its importance in network structure.

Answer: A

NEW QUESTION # 173
......

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