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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	14%	- Data visualization and graph analytics - Distributed and parallel data processing - Exploratory Data Analysis (EDA) - Time-series analysis and anomaly detection
Topic 2: Data Manipulation and Software Literacy	19%	- Data processing libraries selection and usage - GPU-accelerated ETL workflows - Dependency management and containerization - Performance profiling and optimization tools
Topic 3: GPU and Cloud Computing	16%	- Resource management and scaling strategies - GPU architecture and acceleration principles - CRISP-DM and data science methodology - Cloud GPU environments and deployment
Topic 4: Machine Learning	15%	- Model evaluation and validation - Distributed training strategies - Model training and hyperparameter tuning - GPU-accelerated ML frameworks and algorithms
Topic 5: Data Preparation	17%	- Data cleaning, preprocessing and transformation - Feature engineering and data type optimization - Data validation and quality assurance - Workflow monitoring and bottleneck identification
Topic 6: MLOps	19%	- Monitoring, logging and maintenance - Pipeline automation and orchestration - End-to-end workflow management - Model deployment and serving

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q99-Q104):

NEW QUESTION # 99
A machine learning engineer is training a convolutional neural network (CNN) on an NVIDIA GPU and needs to maximize throughput while avoiding OOM errors.
Which of the following techniques is the most effective way to balance memory efficiency and training speed?

A. Loading all dataset samples into GPU memory at the start of training
B. Using a batch size of 1 to minimize memory usage
C. Using dynamic batch sizing based on available GPU memory
D. Allocating a fixed batch size without monitoring memory usage

Answer: C

NEW QUESTION # 100
A team is processing large-scale tabular data using cuDF and cuML on NVIDIA GPUs but is facing performance degradation.
Which of the following techniques would be the most effective in identifying and resolving bottlenecks in the pipeline?

A. Use nvprof or nsight compute to analyze kernel execution time and memory transfer efficiency.
B. Increase GPU clock speed manually to force higher processing power.
C. Reduce the dataset size to a smaller sample to speed up processing.
D. Convert all datasets into pandas DataFrames for initial processing before moving to cuDF.

Answer: A

NEW QUESTION # 101
When performing benchmarking and optimization for GPU-accelerated workflows, which of the following tools is best suited for analyzing the memory utilization and computational efficiency of deep learning models running on Nvidia GPUs?

A. Nvidia Riva
B. Nvidia TensorRT
C. Nvidia Nsight Compute
D. Nvidia CUDA Profiler

Answer: C

NEW QUESTION # 102
In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

A. Lower cost of cloud infrastructure
B. Better for memory-intensive workloads
C. Efficient handling of matrix operations in machine learning models
D. Faster parallel processing for large datasets
E. Lower energy consumption compared to CPUs

Answer: C,D

NEW QUESTION # 103
You are setting up a deep learning model for training on a multi-GPU cluster. You want to maximize training efficiency while maintaining model convergence.
Which of the following strategies is most effective in ensuring efficient multi-GPU training?

A. Reduce batch size to ensure each GPU receives only a small portion of the training data.
B. Train each GPU independently on a different dataset to reduce the communication bottleneck.
C. Use Data Parallelism, where each GPU gets a different portion of the dataset, but gradients are averaged across all GPUs.
D. Use Model Parallelism, where different layers of the model are placed on different GPUs to reduce communication overhead.

Answer: C

NEW QUESTION # 104
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