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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q126-Q131):

NEW QUESTION # 126
When deciding whether to use GPU acceleration or a traditional CPU approach for a machine learning task, which of the following factors should be considered to determine if the data qualifies as "big data" and whether GPU acceleration is beneficial? (Select two)

A. CPU-based machine learning methods are always more effective for small datasets, regardless of the algorithm used.
B. GPU acceleration is beneficial when the dataset can be divided into independent chunks that can be processed in parallel.
C. The complexity of the algorithm being used plays a crucial role in deciding whether to use GPU acceleration, with more complex algorithms benefiting from parallel computation.
D. The dataset must be over 100GB in size to qualify as big data and warrant GPU acceleration.
E. The size of the dataset in terms of rows and columns is irrelevant when determining if it qualifies as big data.

Answer: B,C

NEW QUESTION # 127
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. Increase the batch size significantly to improve GPU utilization.
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. Use mixed-precision inference with TensorRT to accelerate computation.

Answer: D

NEW QUESTION # 128
You are a data scientist working on a large-scale deep learning project that requires significant computational resources. You have the option to run your workloads on a cloud-based GPU instance.
Which of the following statements best describes a key benefit of using cloud-based GPUs for your workload?

A. Cloud-based GPUs provide consistent and predictable performance, identical to on-premise dedicated GPUs.
B. Cloud-based GPUs are always more cost-effective than on-premise GPUs, regardless of workload size and duration.
C. Cloud-based GPUs eliminate all data transfer bottlenecks and latencies when training models on large datasets.
D. Cloud-based GPUs enable scalable resource allocation, allowing you to dynamically increase or decrease GPU instances as needed.

Answer: D

NEW QUESTION # 129
Which of the following techniques are best suited for efficiently processing and organizing large datasets using NVIDIA technologies? (Select two)

A. Leveraging Dask for distributed GPU-based parallel computing
B. Using TensorFlow with custom data pipelines for data loading
C. Storing data directly on an HDD and processing with GPUs
D. Using cuDF for GPU-accelerated DataFrame operations

Answer: A,D

NEW QUESTION # 130
Which of the following Nvidia technologies is primarily used for performing benchmarking and optimizing GPU-accelerated deep learning workflows, especially focusing on model training performance?

A. Nvidia Nsight Systems
B. Nvidia Triton Inference Server
C. Nvidia CUDA
D. Nvidia DeepStream SDK

Answer: A

NEW QUESTION # 131
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