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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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The NVIDIA NCP-ADS Certification is a valuable credential in the modern world. The NVIDIA NCP-ADS certification exam offers a great opportunity for beginners and experienced professionals to validate their skills and knowledge level. With the one certification NVIDIA-Certified-Professional Accelerated Data Science exam you can upgrade your expertise and knowledge.

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q162-Q167):

NEW QUESTION # 162
You are working on a machine learning project that requires training a large XGBoost model on a dataset containing millions of records. Due to the dataset size, training on a CPU-based environment takes an excessively long time. To accelerate the training process, you decide to use NVIDIA RAPIDS.
Which of the following is the best approach to leverage GPU acceleration for training the XGBoost model?

A. Install and use dask-xgboost, which automatically optimizes XGBoost training using GPU acceleration.
B. Use cuML to replace scikit-learn's XGBoost implementation, as cuML supports GPU-accelerated XGBoost training.
C. Store the dataset in Apache Parquet format and load it using pandas to improve training performance.
D. Use XGBoost with the "tree_method": "gpu_hist" parameter to enable GPU acceleration.

Answer: D

NEW QUESTION # 163
You are tasked with cleansing a dataset containing numerical data that has significant outliers.
You're using pandas to identify and appropriately handle these outliers before applying CuDF for accelerated downstream analysis.
Which method effectively manages the numerical outliers while preserving the dataset's integrity for subsequent accelerated analytics?

A. Clip outliers using pandas.Series.clip() based on percentile thresholds (e.g., 5th and 95th percentile).
B. Fill outlier values with mean using pandas.Series.fillna().
C. Replace outliers with zero using pandas.Series.replace().
D. Remove outliers completely using pandas.DataFrame.drop().

Answer: A

NEW QUESTION # 164
A data scientist is training a deep learning model on an NVIDIA GPU but notices that the training speed is not significantly faster than when using a CPU.
Which of the following strategies is the best approach to fully utilize GPU acceleration and optimize training performance?

A. Using only CPU-based data augmentation to keep the GPU dedicated to training
B. Increasing the batch size beyond the GPU's memory limit
C. Disabling cuDNN optimizations to allow more flexibility in kernel execution
D. Using mixed precision training with NVIDIA Tensor Cores

Answer: D

NEW QUESTION # 165
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 Riva
B. Nvidia CUDA Profiler
C. Nvidia Nsight Compute
D. Nvidia TensorRT

Answer: C

NEW QUESTION # 166
Which of the following is the main advantage of using TensorRT for inference in an accelerated data science pipeline?

A. TensorRT is only compatible with image classification models and does not support other model types.
B. TensorRT optimizes deep learning models to run efficiently on NVIDIA GPUs by reducing precision while maintaining accuracy.
C. TensorRT is mainly used for data visualization and not for model inference.
D. TensorRT automatically builds training models from raw data without requiring pre-trained models.

Answer: B

NEW QUESTION # 167
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