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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q191-Q196):

NEW QUESTION # 191
A team of data scientists needs to deploy a machine learning model that depends on specific versions of CUDA and TensorFlow, ensuring it runs consistently across different machines without manually configuring each system.
Which of the following approaches best ensures consistency while leveraging NVIDIA GPUs?

A. Running the model in a local Python virtual environment and copying dependencies manually
B. Compiling all dependencies into the host machine and using system-wide installations
C. Using Docker without GPU support and relying on CPU fallback when running TensorFlow
D. Using NVIDIA Docker (nvidia-docker) to containerize the model and manage GPU dependencies

Answer: D

NEW QUESTION # 192
Which NVIDIA technology is specifically designed for accelerating deep learning workloads in the cloud?

A. NVIDIA Jetson
B. TensorRT
C. NVIDIA A100
D. NVIDIA Tesla

Answer: C

NEW QUESTION # 193
You are tasked with designing and implementing a benchmark to compare the performance of different deep learning frameworks, including TensorFlow, PyTorch, and JAX, using NVIDIA GPUs.
Which of the following is the most effective approach to ensure an accurate and fair comparison?

A. Compare training times only without considering throughput, power efficiency, or memory utilization.
B. Ensure identical hardware configurations, dataset preprocessing, and model architectures while leveraging NVIDIA's Nsight Systems and DLProf for analysis.
C. Run each framework with default settings to compare their out-of-the-box performance without any optimizations.
D. Use mixed precision (FP16) training only in TensorFlow to maximize performance while keeping other frameworks at FP32.

Answer: B

NEW QUESTION # 194
A data scientist is setting up a RAPIDS AI environment for a machine learning project that requires CUDA-enabled libraries and specific package versions to avoid conflicts.
Which of the following approaches best ensures a stable and reproducible environment while leveraging NVIDIA technologies?

A. Use Conda with the rapidsai channel to create an isolated environment that includes cuDF, cuML, and other GPU-accelerated libraries.
B. Install CUDA and RAPIDS AI libraries using OS-level package managers such as apt or yum instead of Conda or Docker.
C. Manually install each required package with pip to ensure the latest versions are used without interference from Conda.
D. Use a system-wide Python installation and install packages globally to ensure consistency across different users.

Answer: A

NEW QUESTION # 195
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. Monitor CPU usage in the system to detect high CPU load that might indicate a bottleneck
B. Increase the batch size of data loading without checking GPU memory usage
C. Use NVIDIA Nsight Systems to profile GPU utilization and identify potential kernel execution inefficiencies
D. Use print() statements in the code to manually track execution times of different operation

Answer: C

NEW QUESTION # 196
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