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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q71-Q76):

NEW QUESTION # 71
Which of the following tools can be used for profiling deep learning models to identify performance bottlenecks and optimize execution on NVIDIA GPUs? (Select two)

A. Python's cProfile
B. TensorBoard
C. DLProf
D. NVIDIA Nsight Systems

Answer: C,D

NEW QUESTION # 72
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 double-precision (FP64) computations to prevent numerical instability in inference
B. Disable batch inference to ensure each sample is processed independently for better accuracy
C. Use TensorRT for model optimization, including precision quantization and layer fusion
D. Increase the number of neurons in each layer to improve the model's accuracy

Answer: C

NEW QUESTION # 73
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. Implement mixed precision training to lower memory usage and speed up computation without losing accuracy.
B. Decrease the size of the model by reducing the number of layers or the number of filters per layer.
C. Use a smaller batch size to reduce the memory requirements per training iteration, allowing for faster training.
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: A,C

NEW QUESTION # 74
A financial services company is deploying an AI-driven risk assessment model using NVIDIA GPUs on a cloud platform. To optimize resource utilization and cost efficiency, they need to determine the best GPU deployment strategy.
Which of the following is the most effective approach?

A. Deploy the model using NVIDIAAI Enterprise with MIG (Multi-Instance GPU) to allocate multiple workloads on a single GPU efficiently.
B. Run all AI workloads on a single large GPU instance without any partitioning or workload separation.
C. Deploy separate full-GPU instances for each workload, even if they have variable compute demands.
D. Choose an on-demand cloud instance with an outdated GPU model to reduce costs, even if performance is compromised.

Answer: A

NEW QUESTION # 75
You are working with structured tabular data in a cloud-based GPU environment.
Your dataset contains the following columns:
Column Name Example Values Data Type Needed
user_id 15432, 98765, 43210 Integer
purchase_amt 12.99, 35.50, 100.75 Floating Point
category 'Books', 'Electronics' Categorical
Which of the following is the most optimal approach to assign data types to these columns to ensure efficient memory usage and computational performance?

A. 1. df['user_id'] = df['user_id'].astype('int64')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')
B. 1. df['user_id'] = df['user_id'].astype('int16')
2. df['purchase_amt'] = df['purchase_amt'].astype('float16')
3. df['category'] = df['category'].astype('string')
C. 1. df['user_id'] = df['user_id'].astype('int32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float32')
3. df['category'] = df['category'].astype('category')
D. 1. df['user_id'] = df['user_id'].astype('float32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')

Answer: C

NEW QUESTION # 76
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