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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q116-Q121):

NEW QUESTION # 116
You are working on a data science project that involves processing large-scale financial transaction data. You want to optimize data manipulation operations using NVIDIA's RAPIDS cuDF.
Which of the following approaches best leverages NVIDIA technologies for efficient data manipulation?

A. Preprocess the data using Apache Spark's CPU-based DataFrame API before transferring it to a GPU for machine learning.
B. Convert the dataset into a SQLite database and execute SQL queries to perform data transformations.
C. Use cuDF DataFrames for data manipulation and rely on GPU-accelerated functions like .groupby(),
.merge(), and .applymap().
D. Load the dataset into a Pandas DataFrame and use multi-threading to speed up operations.

Answer: C

NEW QUESTION # 117
In the CRISP-DM process, which NVIDIA technology is commonly used during the "Data Preparation" phase to handle large datasets efficiently?

A. NVIDIA Jetson
B. NVIDIA RAPIDS
C. NVIDIA Deep Learning AI
D. NVIDIA CUDA Toolkit

Answer: B

NEW QUESTION # 118
Which tools or technologies from NVIDIA are essential for implementing an efficient MLOps pipeline in production environments? (Select two)

A. NVIDIA CUDA for model training in cloud environments
B. NVIDIA NGC for storing and sharing machine learning datasets
C. NVIDIA DLA (Deep Learning Accelerator) for model deployment
D. NVIDIA Triton Inference Server for managing deployment and serving models
E. NVIDIA TensorRT for efficient model inference

Answer: D,E

NEW QUESTION # 119
Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

A. Log Transformation
B. Min-Max Scaling
C. Z-score Standardization
D. Robust Scaling

Answer: D

NEW QUESTION # 120
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 # 121
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