Free NVIDIA-Certified Professional: Accelerated Data Science (NCP-ADS) Practice Questions

Sample NCP-ADS Practice Questions

Browse all 10 free NVIDIA-Certified Professional: Accelerated Data Science practice questions below.

1. A data scientist needs to accelerate pandas-like DataFrame operations on a single GPU. Which RAPIDS library should they use?

   • cuGraph
   • cuSignal
   • cuSpatial
   • cuDF, which provides a pandas-like API for GPU-accelerated DataFrame operations including filtering, groupby, joins, and aggregations

2. When deploying a trained cuML model to production, which NVIDIA platform provides optimized inference serving with dynamic batching and model versioning?

   • NVIDIA DALI, which provides GPU-accelerated data loading and augmentation pipelines that include built-in model serving capabilities for production workloads
   • NVIDIA NeMo, which bundles conversational AI model training with an integrated inference server designed for general-purpose ML model deployment
   • NVIDIA TensorRT alone, which handles model versioning, dynamic batching, and multi-model serving in addition to its core optimization functionality
   • NVIDIA Triton Inference Server, which supports dynamic batching, concurrent model execution, model versioning, and multi-framework serving with GPU-optimized inference pipelines

3. What is the primary advantage of using cuDF over pandas for data cleansing operations on a dataset with 500 million rows?

   • cuDF automatically detects and corrects data quality issues such as inconsistent formatting, duplicate records, and invalid entries without explicit user code
   • cuDF uses less total system memory than pandas because GPU memory compression is more efficient than CPU RAM allocation for tabular data
   • cuDF enables massively parallel processing on the GPU, dramatically reducing computation time for large datasets
   • cuDF provides additional cleansing functions not available in pandas, including proprietary algorithms for outlier detection and automated data imputation

4. Which GPU architecture concept explains why data science workloads with high arithmetic intensity benefit most from GPU acceleration?

   • Direct memory mapping between GPU VRAM and system disk storage, which eliminates the CPU-to-GPU transfer bottleneck entirely
   • The GPU's larger L3 cache compared to CPUs, which ensures that the entire dataset fits in cache for rapid in-memory analytics without hitting DRAM
   • The GPU's superior single-threaded performance compared to CPUs, which accelerates sequential computations in iterative algorithms
   • Massive parallelism through thousands of CUDA cores executing the same operation on different data elements simultaneously

5. A data scientist wants to train a random forest classifier on a GPU using the RAPIDS ecosystem. Which library provides GPU-accelerated implementations of traditional ML algorithms?

   • cuGraph
   • RAPIDS Memory Manager (RMM)
   • cuDF
   • cuML, which offers GPU-accelerated random forest, linear regression, k-means, DBSCAN, PCA, and many other algorithms with a scikit-learn-compatible API

6. What is the primary advantage of the cuDF-pandas accelerator mode?

   • It accelerates existing pandas code on GPUs with zero code changes
   • It converts pandas DataFrames to Apache Arrow format for distributed processing across multiple cluster nodes using MPI communication
   • It replaces the pandas library entirely and removes CPU fallback capabilities to ensure consistent GPU-only execution
   • It requires rewriting all pandas code to use cuDF-specific APIs and function signatures before any GPU acceleration can take effect

7. When using NVIDIA Triton Model Analyzer, which metrics are primarily evaluated to optimize model serving configuration?

   • Throughput, latency, and GPU memory utilization
   • Training loss curves, validation accuracy, and learning rate schedules across different training epochs and hyperparameter combinations
   • Data preprocessing speed, ETL pipeline efficiency, and storage I/O bandwidth for upstream data feeds entering the serving pipeline
   • Network bandwidth between client and server, DNS resolution time, and TLS handshake overhead for each incoming inference request

8. What is NVTabular primarily designed for in GPU-accelerated data science workflows?

   • Managing GPU cluster resources and job scheduling
   • Visualizing high-dimensional data in interactive dashboards with real-time streaming capabilities for monitoring model performance
   • Training deep learning models for computer vision tasks using convolutional neural network architectures with mixed precision support
   • Feature engineering and preprocessing for recommender systems and tabular data at scale on GPUs

9. Which NVIDIA technology enables GPU-accelerated direct data transfer between storage and GPU memory, bypassing the CPU and system memory? (Select TWO)

   • NVIDIA CUDA Unified Memory which automatically migrates pages between CPU and GPU memory on demand based on access patterns
   • NVIDIA TensorRT which optimizes trained neural network models for inference by fusing layers and reducing numerical precision
   • NVIDIA GPUDirect Storage (GDS) which allows a direct data path between local or remote storage and GPU memory
   • cuFile API which provides user-space file I/O operations that leverage GDS for direct GPU memory access
   • NVIDIA Multi-Process Service (MPS) which enables concurrent kernel execution from multiple processes sharing a single GPU context

10. In cuML, how does the GPU-accelerated Random Forest implementation differ from scikit-learn's CPU version?

    • cuML's version only supports binary classification tasks and cannot handle multi-class classification or regression problems unlike scikit-learn's full implementation
    • cuML builds trees in parallel on the GPU, providing significant speedups while offering a scikit-learn-compatible API
    • cuML requires data to be pre-sorted in ascending order by all feature columns before training, while scikit-learn handles unsorted input automatically
    • cuML uses a completely different algorithm that produces fundamentally different results and cannot replicate scikit-learn's output regardless of parameter settings