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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	14%	- Exploratory Data Analysis (EDA) - Distributed and parallel data processing - Time-series analysis and anomaly detection - Data visualization and graph analytics
Topic 2: Data Preparation	17%	- Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification - Feature engineering and data type optimization - Data validation and quality assurance
Topic 3: Machine Learning	15%	- Distributed training strategies - GPU-accelerated ML frameworks and algorithms - Model training and hyperparameter tuning - Model evaluation and validation
Topic 4: GPU and Cloud Computing	16%	- Cloud GPU environments and deployment - GPU architecture and acceleration principles - CRISP-DM and data science methodology - Resource management and scaling strategies
Topic 5: MLOps	19%	- Model deployment and serving - End-to-end workflow management - Monitoring, logging and maintenance - Pipeline automation and orchestration
Topic 6: Data Manipulation and Software Literacy	19%	- Performance profiling and optimization tools - Data processing libraries selection and usage - Dependency management and containerization - GPU-accelerated ETL workflows

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q151-Q156):

NEW QUESTION # 151
You are consulting for a retail company that collects data from daily sales transactions, customer interactions, and inventory tracking across multiple locations. They are unsure whether their dataset qualifies as big data and which processing method would be most suitable.
Which of the following characteristics best indicate that the dataset requires big data processing and acceleration techniques?

A. The dataset is stored in multiple relational database tables, making querying inefficient
B. The dataset exceeds the memory capacity of a single machine and requires distributed or GPU- accelerated processing
C. The dataset contains more than 1 million records, making it impossible to process using pandas
D. The dataset includes a mix of numerical and categorical variables, requiring additional preprocessing

Answer: B

NEW QUESTION # 152
A data scientist is working with large-scale ETL (Extract, Transform, Load) pipelines on GPU- accelerated infrastructure using RAPIDS. The workload involves frequent shuffle operations, which significantly impact performance.
What is the best approach using NVIDIA technologies to reduce shuffle overhead and improve performance?

A. Use RAPIDS cuDF's GPU memory caching to store intermediate DataFrames and avoid redundant shuffle operations.
B. Use RAPIDS cuML to replace shuffle-intensive operations with an ML model that predicts data distribution.
C. Enable CUDA Unified Memory to automatically optimize shuffle performance without manual intervention.
D. Store all intermediate shuffle data in CPU memory using pandas to ensure persistence and reduce GPU load.

Answer: A

NEW QUESTION # 153
You are tasked with processing a large dataset of 100 million records for a deep learning project using NVIDIA technologies. You need to determine the most efficient data processing library for this task to maximize performance and reduce processing time.
Which of the following libraries is best suited for this task?

A. pandas
B. PySpark
C. Dask
D. cuDF

Answer: D

NEW QUESTION # 154
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 on a single GPU machine with no GPU-specific optimization, treating the system as if it were CPU-only.
B. Use Dask with a single GPU, distributing data across multiple workers within the GPU without considering GPU memory limitations.
C. 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.
D. Use Dask with the dask_gpu package to assign data chunks across GPUs manually, without utilizing the distributed.Client.

Answer: C

NEW QUESTION # 155
A data scientist is analyzing large-scale sensor readings from an industrial IoT system and wants to visualize high-frequency time-series data efficiently.
Which approach using NVIDIA technologies would be the most effective for interactive visualization of this dataset?

A. Apply TensorRT to compress the time-series data and use PyTorch's native visualization functions.
B. Use RAPIDS cuDF with Datashader to render large time-series data on the GPU efficiently.
C. Process the data using RAPIDS cuDF, then convert it to CSV and visualize it with Excel charts.
D. Use NVIDIA OptiX to ray-trace the time-series dataset for visualization.

Answer: B

NEW QUESTION # 156
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