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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Benchmarking GPU workflows 2. CRISP-DM workflow execution 3. Docker and Conda environment management
Topic 2: 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)
Topic 3: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Perform time series analysis and visualization 2. Use cuGraph for graph analytics 3. Detect anomalies in time series datasets
Topic 4: MLOps	19%	- Deployment and Monitoring 1. Model deployment in production environments 2. Performance benchmarking and optimization 3. Memory and capacity evaluation
Topic 5: Data Preparation	17%	- Data Cleaning and Transformation 1. cuDF and pandas data preprocessing 2. Synthetic data generation with RAPIDS 3. Data normalization and standardization
Topic 6: Machine Learning	15%	- Model Development and Optimization 1. Feature engineering 2. Multi-GPU training comparison 3. Memory optimization techniques (mixed precision, batching) 4. Hyperparameter tuning

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q156-Q161):

NEW QUESTION # 156
You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.
Which of the following frameworks would be the best choice for handling this workload?

A. Apache Spark with its default CPU-based execution
B. Hadoop MapReduce
C. RAPIDS + Dask for distributed GPU-accelerated ETL
D. Pandas with multiprocessing

Answer: C

NEW QUESTION # 157
What is the primary advantage of using NVIDIA Triton Inference Server for deploying and monitoring machine learning models in production?

A. It is designed solely for edge devices and not for data centers.
B. It only supports TensorFlow models for inference.
C. It provides GPU optimization to handle high-throughput inference workloads.
D. It automatically tunes hyperparameters for all models.

Answer: C

NEW QUESTION # 158
You are developing an end-to-end data pipeline that processes terabytes of image metadata using NVIDIA technologies. You need a software stack that efficiently integrates GPU-accelerated data processing, machine learning, and visualization.
Which of the following tool combinations is best suited for this task?

A. cuDF for data manipulation, cuML for machine learning, and Plotly for visualization.
B. Excel for data analysis, scikit-learn for machine learning, and Seaborn for visualization.
C. Pandas for data manipulation, XGBoost for machine learning, and Matplotlib for visualization.
D. Hadoop for data storage, NumPy for computations, and TensorFlow for visualization.

Answer: A

NEW QUESTION # 159
You are training a machine learning model using scikit-learn-like API on a dataset with millions of samples and thousands of features. You need to optimize both training time and inference speed using NVIDIA technologies.
Which solution is the most appropriate?

A. Use NVIDIA Triton Inference Server to train the model efficiently on a single GPU.
B. Use NVIDIA RAPIDS cuML for GPU-accelerated machine learning model training.
C. Use NVIDIA Modulus to accelerate machine learning training and feature selection.
D. Use NVIDIA Magnum IO to optimize machine learning model parameters on the GPU.

Answer: B

NEW QUESTION # 160
A team of data scientists needs to deploy a machine learning model that depends on specific versions of CUDA and TensorFlow, ensuring it runs consistently across different machines without manually configuring each system.
Which of the following approaches best ensures consistency while leveraging NVIDIA GPUs?

A. Using Docker without GPU support and relying on CPU fallback when running TensorFlow
B. Running the model in a local Python virtual environment and copying dependencies manually
C. Using NVIDIA Docker (nvidia-docker) to containerize the model and manage GPU dependencies
D. Compiling all dependencies into the host machine and using system-wide installations

Answer: C

NEW QUESTION # 161
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