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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- Data Cleaning and Transformation 1. Synthetic data generation with RAPIDS 2. Data normalization and standardization 3. cuDF and pandas data preprocessing
Topic 2: Machine Learning	15%	- Model Development and Optimization 1. Memory optimization techniques (mixed precision, batching) 2. Multi-GPU training comparison 3. Hyperparameter tuning 4. Feature engineering
Topic 3: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Data caching and performance optimization 2. GPU-accelerated ETL design and implementation 3. Distributed data processing frameworks (Dask)
Topic 4: MLOps	19%	- Deployment and Monitoring 1. Memory and capacity evaluation 2. Model deployment in production environments 3. Performance benchmarking and optimization
Topic 5: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Detect anomalies in time series datasets 2. Use cuGraph for graph analytics 3. Perform time series analysis and visualization
Topic 6: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. Benchmarking GPU workflows 3. CRISP-DM workflow execution

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q41-Q46):

NEW QUESTION # 41
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. Monitor CPU usage in the system to detect high CPU load that might indicate a bottleneck
C. Increase the batch size of data loading without checking GPU memory usage
D. Use NVIDIA Nsight Systems to profile GPU utilization and identify potential kernel execution inefficiencies

Answer: D

NEW QUESTION # 42
You are comparing the performance of GPU-accelerated deep learning models on two cloud platforms: AWS EC2 and Google Cloud Platform (GCP). You want to design a benchmark that evaluates GPU resource utilization, processing time, and cost-efficiency for training models with large datasets.
Which actions should you take to implement an effective benchmark? (Select two)

A. Measure both GPU utilization and network latency between the cloud platform and your data storage location.
B. Limit the benchmarking tests to a single model architecture for each cloud platform.
C. Only consider the cost of GPU resources for each cloud provider without factoring in data storage and transfer costs.
D. Run each model on the same GPU type and instance size across both cloud platforms.

Answer: A,D

NEW QUESTION # 43
You are processing large-scale datasets in Dask-cuDF and observe that your computation involves excessive data shuffling, which slows down performance. You decide to implement data caching to reduce shuffle overhead.
Which of the following best describes a technique to reduce shuffle costs in a Dask-cuDF workflow?

A. Using .compute() on each partition immediately forces Dask to materialize results and store them in memory, ensuring shuffle operations are avoided.
B. Disabling lazy execution using dask.config.set(lazy=False) forces all computations to execute eagerly, thereby avoiding shuffle.
C. Splitting the DataFrame into multiple smaller DataFrames and recomputing each separately reduces shuffle operations in Dask-cuDF.
D. Persisting intermediate results in distributed GPU memory using df.persist() ensures that Dask does not recompute partitions, reducing shuffle overhead.

Answer: D

NEW QUESTION # 44
A data scientist is using an NVIDIA RAPIDS-based data processing pipeline on a GPU cluster. They notice that the pipeline is not performing as expected and suspect a bottleneck.
Which of the following approaches would best help identify the source of the bottleneck?

A. Increase the batch size of data being processed without analyzing memory utilization.
B. Disable GPU parallelism and run computations on the CPU to compare performance.
C. Rely on nvidia-smi alone to check GPU utilization without considering other profiling tools.
D. Use nsys (NVIDIA Nsight Systems) to profile GPU kernel execution and memory transfer times.

Answer: D

NEW QUESTION # 45
You are developing an end-to-end data pipeline that processes terabytes of image metadata using NVIDIA technologies. You need a software stack that efficiently integrates GPU-accelerated data processing, machine learning, and visualization.
Which of the following tool combinations is best suited for this task?

A. Hadoop for data storage, NumPy for computations, and TensorFlow for visualization.
B. cuDF for data manipulation, cuML for machine learning, and Plotly for visualization.
C. Excel for data analysis, scikit-learn for machine learning, and Seaborn for visualization.
D. Pandas for data manipulation, XGBoost for machine learning, and Matplotlib for visualization.

Answer: B

NEW QUESTION # 46
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