New NVIDIA NCP-ADS Dumps - NCP-ADS Valid Mock Exam

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Machine Learning	15%	- Model Development and Optimization 1. Memory optimization techniques (mixed precision, batching) 2. Feature engineering 3. Multi-GPU training comparison 4. Hyperparameter tuning
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
Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. Synthetic data generation with RAPIDS 3. cuDF and pandas data preprocessing
GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. CRISP-DM workflow execution 3. Benchmarking GPU workflows
MLOps	19%	- Deployment and Monitoring 1. Performance benchmarking and optimization 2. Memory and capacity evaluation 3. Model deployment in production environments
Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. GPU-accelerated ETL design and implementation 2. Distributed data processing frameworks (Dask) 3. Data caching and performance optimization

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q102-Q107):

NEW QUESTION # 102
A data science team is deploying a deep learning model for real-time inference. The model is optimized for inference on an NVIDIA A100 GPU, but the team notices that inference latency is higher than expected.
Which of the following optimizations is most effective in reducing inference latency?

A. Use mixed-precision inference with TensorRT to accelerate computation.
B. Enable CPU offloading to balance the workload between the CPU and GPU.
C. Reduce the model size by randomly pruning neurons without retraining.
D. Increase the batch size significantly to improve GPU utilization.

Answer: A

NEW QUESTION # 103
You are working with a dataset containing billions of rows and need to perform data transformations, aggregations, and joins efficiently on a single-node GPU-enabled workstation.
Which NVIDIA technology is best suited to optimize performance for these operations?

A. NVIDIA Nsight Compute to profile and optimize the performance of GPU-based aggregations.
B. NVIDIA RAPIDS cuDF to leverage GPU acceleration for large-scale DataFrame operations.
C. NVIDIA Triton Inference Server to accelerate data processing workflows on a single GPU.
D. NVIDIA TensorRT to optimize DataFrame transformations and aggregations using deep learning.

Answer: B

NEW QUESTION # 104
You are working on a large-scale data processing pipeline that involves multi-GPU acceleration using Dask. The dataset is too large to fit into the memory of a single GPU, so you decide to distribute the workload across multiple GPUs using Dask-CUDA.
Which of the following steps is necessary to implement efficient data parallelism across multiple GPUs in a Dask-based workflow?

A. Explicitly initialize a LocalCUDACluster with multiple workers, ensuring each worker is assigned a single GPU.
B. Use dask.dataframe.read_parquet() to load the dataset and let Dask automatically distribute computations across multiple GPUs.
C. Convert Dask DataFrames into Pandas DataFrames to leverage GPU acceleration through Pandas' built-in multi-threading support.
D. Use the DaskExecutor from RAPIDS cuML to offload data processing tasks to distributed GPU workers.

Answer: A

NEW QUESTION # 105
When utilizing GPU instances in a cloud environment for data science, which of the following are common considerations? (Select two)

A. Cost-effective GPU instances always provide the best performance
B. Cloud providers offer dedicated GPU instances for high-performance needs
C. GPUs are suitable for both training and inference tasks but not for preprocessing
D. GPU performance can be degraded by network latency in distributed training

Answer: B,D

NEW QUESTION # 106
You are working with a large-scale social network dataset and need to analyze relationships between users to detect communities using the Louvain algorithm. Given the benefits of GPU acceleration, you decide to use cuGraph for this task.
Which of the following statements best describes why cuGraph is beneficial for this workload?

A. cuGraph primarily accelerates deep learning tasks and is not optimized for graph-based algorithms.
B. Running graph algorithms on a GPU leads to higher latency due to the overhead of data movement between CPU and GPU.
C. cuGraph enables parallel computation on the GPU, significantly speeding up community detection tasks compared to traditional CPU-based implementations.
D. cuGraph does not support weighted graphs, making it less useful for real-world social network analysis.

Answer: C

NEW QUESTION # 107
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