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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q132-Q137):

NEW QUESTION # 132
When performing benchmarking and optimization for GPU-accelerated workflows, which of the following tools is best suited for analyzing the memory utilization and computational efficiency of deep learning models running on Nvidia GPUs?

A. Nvidia TensorRT
B. Nvidia Nsight Compute
C. Nvidia Riva
D. Nvidia CUDA Profiler

Answer: B

NEW QUESTION # 133
You are using NVIDIA DLProf to analyze the performance of a deep learning model deployed on an A100 GPU. The report indicates that compute-bound operations are dominating execution time, and kernel execution efficiency is below 50%.
What is the best action to take based on this insight?

A. Enable mixed precision training to improve computational efficiency.
B. Reduce the number of layers in the model to decrease computation time.
C. Use DLProf's Tensor Core Analysis to check if the model is leveraging Tensor Cores effectively.
D. Increase the batch size to fully utilize available GPU memory and reduce per-sample processing overhead.

Answer: C

NEW QUESTION # 134
A machine learning engineer wants to evaluate the performance of NVIDIA RAPIDS cuDF and Apache Spark for large-scale data processing on a GPU-enabled cluster.
Which of the following strategies is the most effective for obtaining a fair and comprehensive benchmark?

A. Execute identical ETL workflows on cuDF and Spark-RAPIDS and measure execution time and resource utilization.
B. Focus only on processing speed without considering resource consumption differences between frameworks.
C. Run Spark on a CPU cluster while running RAPIDS on a GPU to compare real-world scenarios.
D. Limit the benchmark to small datasets since GPUs excel at parallel processing.

Answer: A

NEW QUESTION # 135
You are tasked with optimizing a data science workflow to scale across multiple GPUs using Dask.
Which of the following approaches would be most effective for implementing data parallelism in this scenario? (Select two)

A. Use dask_cuda's CUDACluster to manage the multi-GPU setup and parallelize computations
B. Use Dask's default scheduler to distribute work across GPUs without any specific configuration
C. Run the computation on a single GPU and let Dask handle CPU parallelism automatically
D. Manually partition the dataset and assign each partition to a specific GPU using dask.delayed
E. Use Dask's distributed scheduler along with dask_cuda to handle GPU resources for parallel processing

Answer: A,E

NEW QUESTION # 136
A data science team is using NVIDIA GPUs to accelerate their AI workflow for a fraud detection system. They follow the CRISP-DM methodology to ensure a structured approach to the project.
During the Data Preparation phase, what is the most effective way to leverage NVIDIA technologies?

A. Use NVIDIA RAPIDS to preprocess large-scale transaction datasets efficiently on GPUs before training the model.
B. Manually clean and transform data using traditional CPU-based processing tools like pandas, then transfer the data to the GPU only for training.
C. Perform all data transformation on a single CPU core to ensure stability before deploying to GPU- accelerated training.
D. Ignore feature engineering and feed raw data directly into the model, relying on deep learning to extract relevant features.

Answer: A

NEW QUESTION # 137
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NCP-ADS Valid Braindumps Book | New NCP-ADS Exam Questions

NVIDIA NCP-ADS Exam Syllabus Topics:

Other NVIDIA NCP-ADS Exam Key Questions

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q132-Q137):