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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation	17%	- Data Cleaning and Transformation 1. cuDF and pandas data preprocessing 2. Data normalization and standardization 3. Synthetic data generation with RAPIDS
Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Detect anomalies in time series datasets 2. Perform time series analysis and visualization 3. Use cuGraph for graph analytics
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)
MLOps	19%	- Deployment and Monitoring 1. Performance benchmarking and optimization 2. Model deployment in production environments 3. Memory and capacity evaluation
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
GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. CRISP-DM workflow execution 3. Benchmarking GPU workflows

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q90-Q95):

NEW QUESTION # 90
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. Disabling cuDNN optimizations to allow more flexibility in kernel execution
C. Using mixed precision training with NVIDIA Tensor Cores
D. Increasing the batch size beyond the GPU's memory limit

Answer: C

NEW QUESTION # 91
A data scientist is deploying a deep learning model to production using an NVIDIA GPU-powered inference server. They want to ensure low latency and high throughput while maintaining model accuracy.
Which of the following deployment strategies would be most effective?

A. Disabling mixed-precision inference to avoid accuracy loss
B. Deploying a model without batching to ensure real-time responses
C. Using NVIDIA Triton Inference Server with model ensemble optimization
D. Running inference on a CPU instead of a GPU to reduce power consumption

Answer: C

NEW QUESTION # 92
Which of the following techniques are commonly used to identify and acquire datasets for machine learning and data science projects? (Select three)

A. Data augmentation
B. Crowdsourcing
C. Manual data entry
D. Web scraping
E. Public APIs

Answer: B,D,E

NEW QUESTION # 93
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. Utilize NVIDIA Morpheus with deep learning-based anomaly detection to process high-throughput log data efficiently.
B. Deploy a TensorRT-optimized Transformer model to process the time-series data in parallel for real- time anomaly detection.
C. Use RAPIDS cuML's GPU-accelerated DBSCAN to cluster the sensor readings and classify outliers as anomalies.
D. Implement a GPU-accelerated spectral residual anomaly detection model using NVIDIA Merlin for rapid feature selection.

Answer: A

NEW QUESTION # 94
You are designing a machine learning pipeline and must decide whether your dataset qualifies as "big data" and requires specialized acceleration methods.
Which of the following characteristics best indicates that your dataset meets the definition of big data?

A. The dataset is too large to fit into the memory (RAM) of a single machine and requires distributed processing.
B. The dataset primarily consists of real-time sensor data that streams continuously but does not exceed local storage limits.
C. The dataset includes millions of small text files stored on a local disk but does not require complex computations.
D. The dataset contains a large number of features (high dimensionality), even if it fits comfortably in local memory.

Answer: A

NEW QUESTION # 95
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