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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Performance benchmarking and optimization 3. Model deployment in production environments
Topic 2: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Distributed data processing frameworks (Dask) 2. Data caching and performance optimization 3. GPU-accelerated ETL design and implementation
Topic 3: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. Benchmarking GPU workflows 3. CRISP-DM workflow execution
Topic 4: 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
Topic 5: 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
Topic 6: Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. Synthetic data generation with RAPIDS 3. cuDF and pandas data preprocessing

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q282-Q287):

NEW QUESTION # 282
You are training a convolutional neural network (CNN) model with a large dataset on a single GPU.
The model is consuming too much GPU memory, and training is slow.
Which of the following techniques would help you reduce GPU memory consumption while maintaining or improving the efficiency of training? (Select two)

A. Decrease the size of the model by reducing the number of layers or the number of filters per layer.
B. Use a smaller batch size to reduce the memory requirements per training iteration, allowing for faster training.
C. Implement mixed precision training to lower memory usage and speed up computation without losing accuracy.
D. Use automatic differentiation for every operation to save memory during the backpropagation phase.
E. Use batch normalization to reduce memory usage by skipping some of the computations during training.

Answer: B,C

NEW QUESTION # 283
You are training a deep learning model on a large dataset of images stored in an Amazon S3 bucket.
You want to optimize data loading, augmentation, and preprocessing on NVIDIA GPUs to avoid CPU bottlenecks.
Which of the following approaches is the most efficient for GPU-accelerated data preprocessing?

A. Use TensorFlow's tf.data API with tf.image transformations and ensure that the preprocessed images are transferred to GPU memory at the end of the pipeline.
B. Use NVIDIA DALI to decode images, apply transformations such as resizing and normalization, and load batches directly to the GPU for training.
C. Load the dataset using PyTorch's torchvision.transforms and DataLoader, leveraging the CPU for data preprocessing and transferring batches to the GPU before training.
D. Use OpenCV to load and preprocess images on the CPU, then transfer the processed images to the GPU before training.

Answer: B

NEW QUESTION # 284
You are a data scientist working on a large-scale deep learning project. Your team needs to train a deep neural network (DNN) on terabytes of image data using NVIDIA GPUs in the cloud.
Which of the following approaches will maximize GPU utilization and optimize training time in an NVIDIA- accelerated cloud environment?

A. Select low-cost, consumer-grade GPUs available in the cloud marketplace to reduce costs.
B. Avoid using mixed precision training and rely solely on full-precision (FP32) computations for numerical stability.
C. Use Jupyter Notebooks on a local machine with minimal GPU resources and transfer model checkpoints manually to the cloud for training.
D. Use NVIDIA NGC containers with TensorFlow or PyTorch and enable NCCL for multi-GPU communication.

Answer: D

NEW QUESTION # 285
You are working with a large dataset containing numeric and categorical features, which will be processed using NVIDIA RAPIDS cuDF for accelerated analytics.
To optimize performance while minimizing memory usage, which data type is the most appropriate for storing a categorical variable with a small number of unique values?

A. category - Optimizes storage and computation for categorical data in cuDF.
B. bool - Minimizes memory usage and supports efficient operations for categorical data.
C. int64 - Provides high precision and avoids potential overflow.
D. float32 - Reduces memory consumption compared to float64 while maintaining precision.

Answer: A

NEW QUESTION # 286
A retail company is deploying an AI-driven demand forecasting system using NVIDIA GPUs. The team follows the CRISP-DM framework and is currently in the Evaluation phase.
Which approach best leverages NVIDIA technologies to assess model performance effectively?

A. Perform evaluation on a small CPU-based subset of the dataset instead of using full GPU-accelerated inference.
B. Use RAPIDS cuML to rapidly compute evaluation metrics like RMSE and R-squared on large datasets using GPUs.
C. Rely only on training loss as the primary evaluation metric without considering validation performance.
D. Assume that a high training accuracy guarantees excellent real-world performance, skipping the evaluation phase.

Answer: B

NEW QUESTION # 287
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