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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. Benchmarking GPU workflows 3. CRISP-DM workflow execution
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. Use cuGraph for graph analytics 2. Detect anomalies in time series datasets 3. Perform time series analysis and visualization
MLOps	19%	- Deployment and Monitoring 1. Performance benchmarking and optimization 2. Model deployment in production environments 3. Memory and capacity evaluation
Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. GPU-accelerated ETL design and implementation 2. Data caching and performance optimization 3. Distributed data processing frameworks (Dask)
Data Preparation	17%	- Data Cleaning and Transformation 1. Synthetic data generation with RAPIDS 2. cuDF and pandas data preprocessing 3. Data normalization and standardization

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

NEW QUESTION # 126
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. Reduce the dataset size to a smaller sample to speed up processing.
B. Convert all datasets into pandas DataFrames for initial processing before moving to cuDF.
C. Increase GPU clock speed manually to force higher processing power.
D. Use nvprof or nsight compute to analyze kernel execution time and memory transfer efficiency.

Answer: D

NEW QUESTION # 127
You are monitoring a GPU-accelerated ETL pipeline using RAPIDS cuDF and Dask-cuDF. You suspect that a bottleneck is causing the pipeline to slow down.
Which of the following methods is the most effective way to diagnose performance bottlenecks in your data processing pipeline?

A. Use print() statements in the code to manually track execution times of different operation
B. Use NVIDIA Nsight Systems to profile GPU utilization and identify potential kernel execution inefficiencies
C. Increase the batch size of data loading without checking GPU memory usage
D. Monitor CPU usage in the system to detect high CPU load that might indicate a bottleneck

Answer: B

NEW QUESTION # 128
You are tasked with profiling a PyTorch-based deep learning model to identify performance bottlenecks using NVIDIA DLProf. Your goal is to analyze kernel execution times and identify operations causing excessive memory consumption.
Which of the following steps is the MOST appropriate sequence for profiling using DLProf?

A. Execute the training script under DLProf TensorBoard mode to visualize performance insights, then re-run the model with automatic mixed precision (AMP) to reduce memory usage.
B. Run dlprof --mode=default --output_path=profile_results on the training script, analyze the generated report, and optimize memory-intensive operations.
C. Use nvidia-smi to capture GPU utilization metrics, then manually correlate high utilization periods with the training script to determine bottlenecks.
D. Profile the model using torch.profiler, then compare the results against the DLProf report to analyze GPU-specific kernel optimizations.

Answer: B

NEW QUESTION # 129
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 RAPIDS cuML to optimize deep learning inference using GPU-accelerated ML algorithms.
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 TensorRT to optimize and accelerate deep learning inference by reducing model size and execution time.

Answer: D

NEW QUESTION # 130
You are conducting rapid experimentation on an NVIDIA GPU to determine the best trade-off between model accuracy and inference latency.
Which approach is the most efficient for systematically evaluating multiple configurations?

A. Train each possible model variation from scratch to evaluate accuracy and performance differences
B. Test different model configurations on a CPU first before moving to the GPU for final evaluation
C. Use automated hyperparameter tuning tools like Optuna or Ray Tune with mixed precision training
D. Reduce training epochs significantly to save time, even if the model is underfitting

Answer: C

NEW QUESTION # 131
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