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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q153-Q158):

NEW QUESTION # 153
A data engineer is preparing a dataset for training a deep learning model. The dataset contains numerical features with missing values, outliers, and inconsistent units.
Which of the following strategies is the most appropriate for ensuring a standardized and clean dataset?

A. Use the median to fill missing values, convert all numerical values into categorical bins, and apply Min-Max scaling.
B. Remove all rows with missing values and outliers to ensure only clean data is used.
C. Standardize the dataset using the mean and standard deviation, but keep missing values and outliers unchanged to avoid data manipulation.
D. Replace missing values with the mean, apply z-score normalization, and clip extreme outliers based on a threshold (e.g., 3 standard deviations).

Answer: D

NEW QUESTION # 154
A company is deploying an MLOps pipeline for training and serving deep learning models. The data scientists want to leverage GPU acceleration at multiple stages of the pipeline to enhance efficiency.
Which of the following steps would benefit the most from GPU acceleration?

A. Storing and retrieving models from a centralized object storage system.
B. Training and inference workloads using deep learning models with TensorFlow or PyTorch.
C. Running CI/CD workflows for code integration and deployment using a traditional CPU-based Jenkins setup.
D. Model monitoring by logging metadata and performance metrics in a database.

Answer: B

NEW QUESTION # 155
A data science team wants to leverage GPU acceleration for detecting anomalies in a massive IoT sensor dataset that is continuously streaming.
Which of the following NVIDIA-supported methods would be the best for handling real-time anomaly detection in a high-throughput environment?

A. Deploy a TensorRT-optimized Transformer model to process the time-series data in parallel for real- time anomaly detection.
B. Use RAPIDS cuML's GPU-accelerated DBSCAN to cluster the sensor readings and classify outliers as anomalies.
C. Implement a GPU-accelerated spectral residual anomaly detection model using NVIDIA Merlin for rapid feature selection.
D. Utilize NVIDIA Morpheus with deep learning-based anomaly detection to process high-throughput log data efficiently.

Answer: D

NEW QUESTION # 156
You are tasked with implementing data caching to reduce shuffle in an accelerated machine learning pipeline using NVIDIA technologies. You need to cache intermediate results after a shuffle operation in a distributed setting.
Which of the following is the best approach to minimize shuffle overhead and maximize performance?

A. Use RAPIDS cuDF to cache the shuffled data on disk and then re-load it from disk during subsequent stages.
B. Use DALI to perform preprocessing and cache the output before the shuffle operation, thereby eliminating the need for shuffle.
C. Cache the data in GPU memory using RAPIDS cuDF for faster access, and leverage GPU-based partitioning to reduce shuffle size.
D. Implement Spark's default disk caching to store shuffle results, allowing the GPU to access disk data directly.

Answer: A

NEW QUESTION # 157
You are working on a time-series forecasting project using NVIDIA RAPIDS and GPU-accelerated machine learning. The dataset consists of 10 years of daily stock price data. Your goal is to implement a model that efficiently handles large-scale time-series data while leveraging GPU acceleration for optimal performance.
Which approach best utilizes NVIDIA technologies for efficient forecasting?

A. Use PyTorch with CPU acceleration to train a convolutional neural network (CNN) for forecasting.
B. Use Dask with pandas for data preprocessing, then train a TensorFlow LSTM model on the CPU.
C. Use cuDF to load and preprocess the data, then apply FB Prophet for forecasting.
D. Use cuDF for data preprocessing and train an XGBoost model with GPU acceleration for forecasting.

Answer: D

NEW QUESTION # 158
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