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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- Data validation and quality assurance - Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification - Feature engineering and data type optimization
Topic 2: Data Analysis	14%	- Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA) - Data visualization and graph analytics - Distributed and parallel data processing
Topic 3: Machine Learning	15%	- Model training and hyperparameter tuning - Distributed training strategies - Model evaluation and validation - GPU-accelerated ML frameworks and algorithms
Topic 4: MLOps	19%	- End-to-end workflow management - Model deployment and serving - Monitoring, logging and maintenance - Pipeline automation and orchestration
Topic 5: Data Manipulation and Software Literacy	19%	- Data processing libraries selection and usage - GPU-accelerated ETL workflows - Performance profiling and optimization tools - Dependency management and containerization
Topic 6: GPU and Cloud Computing	16%	- CRISP-DM and data science methodology - Cloud GPU environments and deployment - Resource management and scaling strategies - GPU architecture and acceleration principles

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q131-Q136):

NEW QUESTION # 131
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 Magnum IO to optimize machine learning model parameters on the GPU.
B. Use NVIDIA RAPIDS cuML for GPU-accelerated machine learning model training.
C. Use NVIDIA Triton Inference Server to train the model efficiently on a single GPU.
D. Use NVIDIA Modulus to accelerate machine learning training and feature selection.

Answer: B

NEW QUESTION # 132
Which of the following is the best approach for performing benchmarking and optimizing GPU- accelerated workflows for MLOps using Nvidia technologies?

A. Rely exclusively on the nvidia-smi tool for monitoring GPU utilization and memory usage across multiple GPUs without making any other performance adjustments.
B. Use TensorRT to optimize deep learning models by converting them into highly optimized inference engines, allowing faster execution with lower latency.
C. Use Nvidia's nsight tools to benchmark only the model training phase and ignore the inference phase, as training is the primary bottleneck.
D. Use Nvidia's nvprof tool to profile GPU resource usage and identify bottlenecks, then adjust the batch size to optimize throughput.

Answer: D

NEW QUESTION # 133
You are training a deep learning model on a large dataset and are deciding whether to use a single GPU or multiple GPUs.
Which of the following are true considerations when comparing single-GPU and multi-GPU training setups? (Select two)

A. Single-GPU training is limited by the VRAM (video memory) on the GPU, so larger models or datasets may require multi-GPU setups.
B. Multi-GPU setups perform better only when the batch size is reduced.
C. Multi-GPU training requires modifications to the model architecture to make it compatible with parallel processing.
D. Multi-GPU training can significantly reduce training time when the dataset is large and the model is computationally intensive.
E. Single-GPU training is generally more cost-effective and should be preferred unless scaling is absolutely necessary.

Answer: A,D

NEW QUESTION # 134
You are working on a large-scale machine learning workload that involves training a deep learning model using multiple GPUs. You want to leverage Dask to implement data parallelism efficiently using NVIDIA GPUs.
Which of the following approaches best achieves data parallelism in this context?

A. Use Dask DataFrame to parallelize deep learning model training across multiple GPUs
B. Use Dask with CuPy to distribute NumPy-based computations across multiple GPUs
C. Leverage Dask-CUDA to automatically assign computations to available GPUs using the worker pool
D. Run a single large Dask task on the CPU and use Dask-MPI for multi-GPU execution

Answer: C

NEW QUESTION # 135
You are working on a large-scale machine learning project that requires preprocessing terabytes of structured and semi-structured data. You need a distributed data processing framework that can leverage NVIDIA GPUs efficiently to accelerate computations.
Which of the following approaches would best achieve this goal?

A. Using Dask with RAPIDS cuDF and cuML for distributed GPU-accelerated processing
B. Using Apache Spark with PySpark for CPU-based distributed data processing
C. Using TensorFlow's Dataset API to load and preprocess the data on GPUs
D. Using plain NumPy with CUDA extensions to manually parallelize computations across multiple GPUs

Answer: A

NEW QUESTION # 136
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