NVIDIA Latest NCP-ADS Practice Questions: NVIDIA-Certified-Professional Accelerated Data Science - VCETorrent Good-reputation Website

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q189-Q194):

NEW QUESTION # 189
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. Use batch normalization to reduce memory usage by skipping some of the computations during training.
B. Use a smaller batch size to reduce the memory requirements per training iteration, allowing for faster training.
C. Use automatic differentiation for every operation to save memory during the backpropagation phase.
D. Decrease the size of the model by reducing the number of layers or the number of filters per layer.
E. Implement mixed precision training to lower memory usage and speed up computation without losing accuracy.

Answer: B,E

NEW QUESTION # 190
You are implementing a Dask-based solution for distributed data parallelism across a multi-GPU system.
Which configuration steps would ensure effective use of GPUs for parallel computation? (Select two)

A. Create a LocalCUDACluster and manually specify the GPUs you want to use for each Dask worker
B. Use dask_cuda's LocalCUDACluster with proper GPU memory management to handle multiple GPUs
C. Use dask_cudf to convert DataFrame computations into GPU-accelerated operations using cuDF
D. Use Dask's Cluster class with the distributed scheduler and specify CPU cores only for GPU workloads
E. Use dask_cuda's LocalCUDACluster and let Dask automatically allocate GPUs without any configuration

Answer: B,C

NEW QUESTION # 191
You have deployed a deep learning model for image classification in a production environment, but inference latency is high. You need to optimize the model to reduce response time while maintaining accuracy.
Which NVIDIA technology is best suited for this task?

A. NVIDIA TensorRT to optimize and accelerate deep learning inference by reducing model size and execution time.
B. NVIDIA Clara Imaging to improve deep learning inference for image classification workloads.
C. NVIDIA DeepStream to process image classification models for low-latency inference in batch mode.
D. NVIDIA RAPIDS cuML to optimize deep learning inference using GPU-accelerated ML algorithms.

Answer: A

NEW QUESTION # 192
You need to deploy a containerized machine learning model that utilizes NVIDIA GPUs on a cloud- based Kubernetes cluster.
Which of the following steps is essential for ensuring proper GPU utilization inside a Docker container?

A. Install GPU drivers inside the container to ensure access to the host's hardware
B. Use the NVIDIA Container Toolkit (nvidia-docker2) to allow GPU access within Docker containers
C. Run the container using the default Docker runtime without any additional configurations
D. Use a standard Python-based container image instead of an NVIDIA GPU-optimized image

Answer: B

NEW QUESTION # 193
A data scientist is working with large-scale ETL (Extract, Transform, Load) pipelines on GPU- accelerated infrastructure using RAPIDS. The workload involves frequent shuffle operations, which significantly impact performance.
What is the best approach using NVIDIA technologies to reduce shuffle overhead and improve performance?

A. Use RAPIDS cuDF's GPU memory caching to store intermediate DataFrames and avoid redundant shuffle operations.
B. Use RAPIDS cuML to replace shuffle-intensive operations with an ML model that predicts data distribution.
C. Enable CUDA Unified Memory to automatically optimize shuffle performance without manual intervention.
D. Store all intermediate shuffle data in CPU memory using pandas to ensure persistence and reduce GPU load.

Answer: A

NEW QUESTION # 194
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