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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 2: MLOps	19%	- Deployment and Monitoring 1. Model deployment in production environments 2. Memory and capacity evaluation 3. Performance benchmarking and optimization
Topic 3: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. CRISP-DM workflow execution 3. Benchmarking GPU workflows
Topic 4: Machine Learning	15%	- Model Development and Optimization 1. Multi-GPU training comparison 2. Feature engineering 3. Hyperparameter tuning 4. Memory optimization techniques (mixed precision, batching)
Topic 5: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Use cuGraph for graph analytics 2. Detect anomalies in time series datasets 3. Perform time series analysis and visualization
Topic 6: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Data caching and performance optimization 2. Distributed data processing frameworks (Dask) 3. GPU-accelerated ETL design and implementation

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q263-Q268):

NEW QUESTION # 263
You are deploying a deep learning model on an edge device with 8GB of available RAM. The model's estimated peak memory usage, including model weights, intermediate tensors, and batch data, is 9.5GB.
What is the best course of action to ensure successful deployment while maintaining performance?

A. Reduce the batch size during inference
B. Reduce the number of model parameters by removing layers from the architecture
C. Increase the device's swap space to compensate for insufficient RAM
D. Offload some computation to cloud-based processing

Answer: A

NEW QUESTION # 264
You are working with a deep learning model for real-time inference on an NVIDIA GPU. Your objective is to optimize inference speed while maintaining acceptable model accuracy.
Which of the following techniques provides the best balance between inference performance and accuracy?

A. Use TensorRT for model optimization, including precision quantization and layer fusion
B. Use double-precision (FP64) computations to prevent numerical instability in inference
C. Disable batch inference to ensure each sample is processed independently for better accuracy
D. Increase the number of neurons in each layer to improve the model's accuracy

Answer: A

NEW QUESTION # 265
You are tasked with optimizing a data science workflow to scale across multiple GPUs using Dask.
Which of the following approaches would be most effective for implementing data parallelism in this scenario? (Select two)

A. Run the computation on a single GPU and let Dask handle CPU parallelism automatically
B. Use dask_cuda's CUDACluster to manage the multi-GPU setup and parallelize computations
C. Manually partition the dataset and assign each partition to a specific GPU using dask.delayed
D. Use Dask's distributed scheduler along with dask_cuda to handle GPU resources for parallel processing
E. Use Dask's default scheduler to distribute work across GPUs without any specific configuration

Answer: B,D

NEW QUESTION # 266
You are training a deep learning model for image classification and want to optimize its hyperparameters, including learning rate, batch size, and number of layers.
Which of the following techniques is the most effective for efficiently searching through a high- dimensional hyperparameter space?

A. Grid Search
B. Random Search
C. Gradient Descent
D. Bayesian Optimization

Answer: D

NEW QUESTION # 267
A machine learning engineer is training a large transformer-based model for natural language processing (NLP). They want to maximize training speed and efficiency using NVIDIA GPUs.
Which of the following techniques would most effectively enhance GPU utilization and reduce training time?

A. Disabling data parallelism
B. Using mixed-precision training with Tensor Cores
C. Prefetching data with the CPU while training on the GPU
D. Running training exclusively on CPU

Answer: B

NEW QUESTION # 268
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