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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q283-Q288):

NEW QUESTION # 283
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 AI Enterprise to directly acquire and store MRI images from multiple hospitals.
B. Use NVIDIA Clara Imaging to standardize, curate, and preprocess MRI datasets for AI model training.
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: B

NEW QUESTION # 284
You are working with a large-scale social network dataset and need to analyze relationships between users to detect communities using the Louvain algorithm. Given the benefits of GPU acceleration, you decide to use cuGraph for this task.
Which of the following statements best describes why cuGraph is beneficial for this workload?

A. cuGraph does not support weighted graphs, making it less useful for real-world social network analysis.
B. cuGraph enables parallel computation on the GPU, significantly speeding up community detection tasks compared to traditional CPU-based implementations.
C. Running graph algorithms on a GPU leads to higher latency due to the overhead of data movement between CPU and GPU.
D. cuGraph primarily accelerates deep learning tasks and is not optimized for graph-based algorithms.

Answer: B

NEW QUESTION # 285
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. Replace missing values with the mean, apply z-score normalization, and clip extreme outliers based on a threshold (e.g., 3 standard deviations).
D. Standardize the dataset using the mean and standard deviation, but keep missing values and outliers unchanged to avoid data manipulation.

Answer: C

NEW QUESTION # 286
A data engineer is designing an Extract, Transform, Load (ETL) pipeline for a retail analytics platform that processes millions of customer transactions per day. The primary objective is to accelerate data ingestion, transformation, and storage while ensuring efficient scalability.
Which of the following approaches would be the most effective for optimizing this ETL workflow using NVIDIA-accelerated ETL tools?

A. Implement ETL processes using only SQL-based transformations within a relational database system.
B. Use Apache Spark with CPU-based processing instead of leveraging GPU acceleration.
C. Use NVIDIA RAPIDS cuDF for data transformations and Dask-cuDF for parallelized processing across multiple GPUs.
D. Perform all transformations using Pandas DataFrames and then use multiprocessing to parallelize the workload on CPUs.

Answer: C

NEW QUESTION # 287
Which of the following is the most efficient way to implement data parallelism using Dask for multi- GPU scaling on an Nvidia platform?

A. Use Dask with a single GPU, distributing data across multiple workers within the GPU without considering GPU memory limitations.
B. Use Dask with the dask_cuda package to distribute computation across GPUs, ensuring that each GPU is responsible for a portion of the data, while using distributed.Client to connect the GPU workers.
C. Use Dask on a single GPU machine with no GPU-specific optimization, treating the system as if it were CPU-only.
D. Use Dask with the dask_gpu package to assign data chunks across GPUs manually, without utilizing the distributed.Client.

Answer: B

NEW QUESTION # 288
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