Quiz 2026 NCP-ADS: NVIDIA-Certified-Professional Accelerated Data Science Newest Test Study Guide

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation	17%	- Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables - Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues
MLOps	19%	- Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows - Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools
Machine Learning	15%	- Feature engineering and hyperparameter tuning 1. Feature engineering for ML models 2. Batching and memory-efficient training methods 3. Hyperparameter tuning techniques - Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch - Model training with GPU acceleration 1. Multi-GPU training strategies 2. Training models using cuML and GPU-accelerated XGBoost 3. Selection of appropriate algorithms for GPU execution
Data Analysis	14%	- Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals
GPU and Cloud Computing	16%	- GPU architecture and fundamentals 1. GPU architecture fundamentals for data science 2. CPU vs GPU workloads and memory transfer optimization - Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud - GPU resource management 1. Efficient GPU resource allocation and scheduling - Performance optimization 1. Memory profiling with DLProf 2. Mixed precision and bottleneck analysis 3. Single and multi-GPU performance optimization
Data Manipulation and Software Literacy	19%	- Distributed computing with Dask 1. Scaling data operations across multiple GPUs 2. Dask-cuDF for parallel data processing - GPU-accelerated data manipulation using cuDF 1. cuDF vs pandas API mapping and usage 2. Groupby, apply, and aggregation operations 3. Data integration, joining, merging, and filtering - Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q48-Q53):

NEW QUESTION # 48
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. Use nvidia-smi to capture GPU utilization metrics, then manually correlate high utilization periods with the training script to determine bottlenecks.
B. Profile the model using torch.profiler, then compare the results against the DLProf report to analyze GPU-specific kernel optimizations.
C. Run dlprof --mode=default --output_path=profile_results on the training script, analyze the generated report, and optimize memory-intensive operations.
D. 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.

Answer: C

NEW QUESTION # 49
You are working with a large dataset in NVIDIA RAPIDS cuDF and notice that the data processing pipeline is taking longer than expected.
Which of the following tools or techniques can help you identify and analyze bottlenecks in the pipeline?

A. Use nvtx.annotate() to label sections of code and analyze performance in Nsight Systems.
B. Enable pandas compatibility mode in cudf to improve performance.
C. Use cudf.DataFrame.astype(str) to convert numerical values to strings for easier debugging.
D. Use cuml.preprocessing.StandardScaler() to normalize the dataset before processing.

Answer: A

NEW QUESTION # 50
In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)

A. Better for memory-intensive workloads
B. Lower energy consumption compared to CPUs
C. Faster parallel processing for large datasets
D. Efficient handling of matrix operations in machine learning models
E. Lower cost of cloud infrastructure

Answer: C,D

NEW QUESTION # 51
When deciding whether to use GPU acceleration or a traditional CPU approach for a machine learning task, which of the following factors should be considered to determine if the data qualifies as "big data" and whether GPU acceleration is beneficial? (Select two)

A. GPU acceleration is beneficial when the dataset can be divided into independent chunks that can be processed in parallel.
B. The complexity of the algorithm being used plays a crucial role in deciding whether to use GPU acceleration, with more complex algorithms benefiting from parallel computation.
C. The dataset must be over 100GB in size to qualify as big data and warrant GPU acceleration.
D. CPU-based machine learning methods are always more effective for small datasets, regardless of the algorithm used.
E. The size of the dataset in terms of rows and columns is irrelevant when determining if it qualifies as big data.

Answer: A,B

NEW QUESTION # 52
Which of the following is the most efficient way to implement data parallelism using Dask for multi- GPU scaling on an Nvidia platform?

A. Use Dask with the dask_gpu package to assign data chunks across GPUs manually, without utilizing the distributed.Client.
B. Use Dask with a single GPU, distributing data across multiple workers within the GPU without considering GPU memory limitations.
C. Use Dask with the dask_cuda package to distribute computation across GPUs, ensuring that each GPU is responsible for a portion of the data, while using distributed.Client to connect the GPU workers.
D. Use Dask on a single GPU machine with no GPU-specific optimization, treating the system as if it were CPU-only.

Answer: C

NEW QUESTION # 53
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