NCP-ADS Valid Test Labs & Hot NCP-ADS Spot Questions

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q264-Q269):

NEW QUESTION # 264
You are processing a large-scale transportation network graph using NVIDIA cuGraph. The graph is extremely large, consuming almost all available GPU memory. Performance is deteriorating, and some computations fail due to memory exhaustion.
What is the best approach to efficiently handle this large graph while keeping computations on the GPU?

A. Store the graph as a large Python dictionary and use cuGraph only for specific queries.
B. Manually split the graph into chunks and process each chunk separately without any coordination.
C. Convert the graph into a NetworkX graph and process it on the CPU to reduce GPU memory usage.
D. Use cuGraph's multi-GPU support via Dask-cuGraph to distribute the graph across multiple GPUs.

Answer: D

NEW QUESTION # 265
Which of the following tools or techniques are essential for effectively working with large-scale data in a distributed environment? (Select two)

A. Using Excel to manipulate large datasets
B. Using Dask for parallel processing of large datasets
C. Using SQLite as a local database for large-scale data analysis
D. Using Apache Spark for distributed data processing
E. Using SQLAlchemy to interact with databases for large data processing

Answer: B,D

NEW QUESTION # 266
You are comparing the performance of NVIDIA RAPIDS cuML, TensorFlow, and PyTorch for training and inference on a dataset with millions of records.
To design a fair and effective benchmark, which approach should you take?

A. Run each framework on different GPUs to maximize available resources and compare execution times across different hardware configurations.
B. Ensure all frameworks run on the same GPU, use optimized batch sizes, and measure execution time and memory usage with NVIDIA Nsight Systems.
C. Use only a CPU baseline for comparison to demonstrate the benefits of GPU acceleration, ignoring GPU-specific optimizations.

Answer: B

NEW QUESTION # 267
You are working with a large dataset containing customer transactions and want to perform exploratory data analysis (EDA) efficiently. Given the dataset's size, you decide to use NVIDIA RAPIDS to accelerate the process.
Which of the following approaches is the most effective for conducting EDA using NVIDIA technologies?

A. Load the dataset into Pandas and use Matplotlib for visualization
B. Perform SQL queries on a CPU-based database for initial data analysis before GPU acceleration
C. Use TensorFlow and Keras to preprocess the data before performing EDA
D. Use RAPIDS cuDF to perform fast dataframe operations and visualize results with cuXfilter

Answer: D

NEW QUESTION # 268
Which of the following is the best approach for performing benchmarking and optimizing GPU- accelerated workflows for MLOps using Nvidia technologies?

A. Use Nvidia's nvprof tool to profile GPU resource usage and identify bottlenecks, then adjust the batch size to optimize throughput.
B. Use TensorRT to optimize deep learning models by converting them into highly optimized inference engines, allowing faster execution with lower latency.
C. Use Nvidia's nsight tools to benchmark only the model training phase and ignore the inference phase, as training is the primary bottleneck.
D. Rely exclusively on the nvidia-smi tool for monitoring GPU utilization and memory usage across multiple GPUs without making any other performance adjustments.

Answer: A

NEW QUESTION # 269
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