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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q268-Q273):

NEW QUESTION # 268
A manufacturing company wants to detect anomalies in a real-time IoT sensor dataset using NVIDIA technologies. The data arrives as a continuous stream, and the company needs to process it efficiently for immediate anomaly detection.
Which NVIDIA-powered approach is the most suitable for this use case?

A. Leverage cuGraph to model the sensor readings as a graph and detect anomalies using centrality measures
B. Use RAPIDS cuDF with batch processing to analyze historical anomalies every few hours
C. Apply cuML's Isolation Forest on incoming streaming data for real-time anomaly detection
D. Deploy an Autoencoder-based deep learning model using NVIDIA TensorRT for real-time inference

Answer: C

NEW QUESTION # 269
Which of the following Nvidia technologies is commonly used for deploying machine learning models in production environments, enabling scalable deployment and monitoring?

A. Nvidia CUDA
B. Nvidia DeepStream
C. Nvidia Triton Inference Server
D. Nvidia Jetson Nano

Answer: C

NEW QUESTION # 270
A data scientist wants to compare the performance of two different GPU-accelerated data science frameworks, NVIDIA RAPIDS (cuDF, cuML) and TensorFlow, for a tabular data classification task.
Which of the following approaches would be the best practice for designing an unbiased and effective benchmark?

A. Use TensorFlow's built-in training time metrics without comparing equivalent RAPIDS-based operations.
B. Ignore preprocessing and focus only on model training speed when comparing performance.
C. Run all benchmarks on a CPU to ensure fairness across frameworks.
D. Measure execution time and memory usage for each framework using NVIDIA Nsight Systems (nsys).

Answer: D

NEW QUESTION # 271
You are working on a data processing pipeline using NVIDIA GPUs for accelerating computations.
You need to monitor the pipeline's performance to identify bottlenecks.
Which of the following tools or techniques can be used to efficiently recognize bottlenecks in such a GPU-accelerated pipeline? (Select two)

A. NVIDIA CUDA Profiler (nvprof)
B. NVIDIA DLA (Deep Learning Accelerator)
C. NVIDIA Nsight Systems
D. NVIDIA TensorRT Profiling
E. NVIDIA nvidia-smi

Answer: A,C

NEW QUESTION # 272
A machine learning engineer wants to deploy a GPU-accelerated inference model in a containerized environment while ensuring compatibility with NVIDIA libraries.
Which of the following is the best approach for managing dependencies?

A. Run Docker containers without any special configurations, as Docker automatically detects and utilizes GPUs.
B. Install GPU drivers directly inside the container instead of on the host system to avoid dependency conflicts.
C. Use the official NVIDIA Docker base images (nvidia/cuda) and install RAPIDS AI libraries within the container to ensure GPU compatibility.
D. Disable the --gpus flag when running Docker containers, as RAPIDS AI libraries do not require explicit GPU selection.

Answer: C

NEW QUESTION # 273
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