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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Machine Learning	15%	- Model Development and Optimization 1. Memory optimization techniques (mixed precision, batching) 2. Hyperparameter tuning 3. Feature engineering 4. Multi-GPU training comparison
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
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
Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. Synthetic data generation with RAPIDS 3. cuDF and pandas data preprocessing
MLOps	19%	- Deployment and Monitoring 1. Performance benchmarking and optimization 2. Memory and capacity evaluation 3. Model deployment in production environments
GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Benchmarking GPU workflows 2. Docker and Conda environment management 3. CRISP-DM workflow execution

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q158-Q163):

NEW QUESTION # 158
You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.
Which of the following frameworks would be the best choice for handling this workload?

A. RAPIDS + Dask for distributed GPU-accelerated ETL
B. Apache Spark with its default CPU-based execution
C. Pandas with multiprocessing
D. Hadoop MapReduce

Answer: A

NEW QUESTION # 159
You are optimizing a data pipeline for a large-scale machine learning project using NVIDIA RAPIDS and Apache Spark. The pipeline performs many expensive shuffle operations.
Which of the following is the most effective method to reduce shuffle and improve performance using NVIDIA technologies?

A. Store data in HDFS before performing shuffle operations to reduce GPU memory overhead.
B. Use RAPIDS cuDF to perform in-memory data processing on GPU before shuffling to avoid network communication.
C. Use RAPIDS cuDF to repartition the data in the GPU memory after each shuffle.
D. Implement a custom shuffle partitioning scheme using NVIDIA DALI for more control over data partitioning during shuffle operations.

Answer: B

NEW QUESTION # 160
You are developing an AI model for medical imaging that requires acquiring a large dataset of MRI scans from multiple sources.
Which NVIDIA technology would best assist in acquiring, standardizing, and efficiently handling the dataset?

A. Use NVIDIA Clara Imaging to standardize, curate, and preprocess MRI datasets for AI model training.
B. Use NVIDIA AI Enterprise to directly acquire and store MRI images from multiple hospitals.
C. Use NVIDIA Nsight Systems to collect MRI data and visualize potential performance bottlenecks.
D. Use NVIDIA Morpheus to analyze MRI data for cybersecurity threats before storing it.

Answer: A

NEW QUESTION # 161
Which of the following best describes the purpose of the NVIDIA TensorRT library?

A. Provides hardware abstraction for AI model development
B. Optimizes and accelerates inference of trained models
C. Manages GPU resources for deep learning models
D. Accelerates training of neural networks

Answer: B

NEW QUESTION # 162
You are working on a large dataset for a machine learning model and need to preprocess the data efficiently using NVIDIA RAPIDS cuDF on a GPU-accelerated system.
Which of the following statements is correct regarding data preparation using cuDF?

A. cuDF can be used as a direct replacement for Pandas, but it is optimized for multi-node distributed computing rather than GPU acceleration.
B. cuDF supports common Pandas-like operations, such as filtering, merging, and groupby, but performs them efficiently on the GPU.
C. cuDF requires all input data to be stored in Apache Parquet format, as it does not support CSV or JSON ingestion.
D. cuDF operations are executed lazily and require an explicit compute() call to execute transformations.

Answer: B

NEW QUESTION # 163
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