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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Machine Learning	15%	- Model Development and Optimization 1. Multi-GPU training comparison 2. Hyperparameter tuning 3. Feature engineering 4. Memory optimization techniques (mixed precision, batching)
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 Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Data caching and performance optimization 2. GPU-accelerated ETL design and implementation 3. Distributed data processing frameworks (Dask)
MLOps	19%	- Deployment and Monitoring 1. Model deployment in production environments 2. Memory and capacity evaluation 3. Performance benchmarking and optimization
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 Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. cuDF and pandas data preprocessing 3. Synthetic data generation with RAPIDS

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q18-Q23):

NEW QUESTION # 18
You are working with a GPU-based cloud environment and need to optimize the memory usage for a dataset that contains a column item_id representing unique product IDs. The item_id values are large integers, and there are over 10 million distinct product IDs.
Which of the following is the most memory-efficient data type choice for this column?

A. df['item_id'] = df['item_id'].astype('float64')
B. df['item_id'] = df['item_id'].astype('int64')
C. df['item_id'] = df['item_id'].astype('int32')
D. df['item_id'] = df['item_id'].astype('string')

Answer: B

NEW QUESTION # 19
You are working on a large-scale machine learning pipeline that involves processing massive datasets using multiple GPUs on an NVIDIA DGX system. You choose to use Dask to enable efficient parallel processing across multiple GPUs.
Which of the following steps is essential to correctly configure Dask for multi-GPU acceleration?

A. Assign computation tasks explicitly to CPUs using dask.config.set({'scheduler': 'threads'}) before using GPUs.
B. Avoid using dask_cudf and instead rely on standard pandas DataFrames to ensure GPU-accelerated execution.
C. Use dask_cuda.LocalCUDACluster() to create a cluster of GPU workers and pass it to the Dask client.
D. Use dask.distributed.Client() without specifying a scheduler to automatically detect available GPUs.

Answer: C

NEW QUESTION # 20
You are working with a time-series dataset containing network traffic logs. You suspect the presence of anomalies, such as distributed denial-of-service (DDoS) attacks or sudden traffic surges.
Which machine learning approach is best suited for detecting these anomalies?

A. Autoencoder-based Anomaly Detection
B. k-Nearest Neighbors (k-NN) for Classification
C. Linear Regression
D. Naive Bayes Classifier

Answer: A

NEW QUESTION # 21
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. Use RAPIDS cuML's XGBoost-GPU implementation to forecast time-series patterns with improved training efficiency.
B. Convert time-series data to tensors and train a deep learning model using PyTorch with mixed precision on an NVIDIA GPU.
C. Run Facebook Prophet on a multi-core CPU environment to leverage parallel forecasting.
D. Use RAPIDS cuDF and Dask-cuDF to preprocess the data and train an LSTM model with PyTorch Lightning on multiple GPUs.

Answer: D

NEW QUESTION # 22
A financial institution is using cuGraph to analyze transaction data and detect potential fraudulent activity. The institution wants to identify users who have a high likelihood of being involved in suspicious activities based on the structure of their transactions.
Which of the following cuGraph algorithms would be the best choice for this task?

A. Weakly Connected Components
B. Spectral Clustering
C. Betweenness Centrality
D. Breadth-First Search (BFS)

Answer: C

NEW QUESTION # 23
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