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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q243-Q248):

NEW QUESTION # 243
A data science team is deploying a deep learning model for real-time inference. The model is optimized for inference on an NVIDIA A100 GPU, but the team notices that inference latency is higher than expected.
Which of the following optimizations is most effective in reducing inference latency?

A. Enable CPU offloading to balance the workload between the CPU and GPU.
B. Use mixed-precision inference with TensorRT to accelerate computation.
C. Reduce the model size by randomly pruning neurons without retraining.
D. Increase the batch size significantly to improve GPU utilization.

Answer: B

NEW QUESTION # 244
You are working with a large time-series dataset consisting of millions of records and want to efficiently visualize trends over time using NVIDIA technologies. The dataset is stored as a cuDF DataFrame, and you need to generate an interactive line plot with minimal performance overhead.
Which of the following is the best approach to achieve this goal?

A. Use the Bokeh library to plot the time-series data from a cuDF DataFrame directly
B. Load the data into a Spark DataFrame and visualize using Apache Zeppelin
C. Use the hvPlot library with RAPIDS cuDF to directly render the time-series data interactively
D. Convert the cuDF DataFrame to a Pandas DataFrame and plot using Matplotlib

Answer: C

NEW QUESTION # 245
A data scientist is working on a customer segmentation model using NVIDIA RAPIDS on GPUs. The dataset contains millions of customer records with features such as transaction history, age, location, and frequency of visits.
To optimize feature engineering using NVIDIA technologies, what is the best approach?

A. Store all numerical features in float64 format to prevent rounding errors during transformations.
B. Perform feature engineering on CPUs using pandas before transferring data to the GPU for training.
C. Convert all categorical variables into string representations to preserve their original format for later analysis.
D. Use cuDF to perform feature transformations like normalization and one-hot encoding directly on the GPU.

Answer: D

NEW QUESTION # 246
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 includes a mix of numerical and categorical variables, requiring additional preprocessing
C. The dataset exceeds the memory capacity of a single machine and requires distributed or GPU- accelerated processing
D. The dataset contains more than 1 million records, making it impossible to process using pandas

Answer: C

NEW QUESTION # 247
You are working on a large dataset (several terabytes in size) and need to perform data preprocessing, filtering, and transformations before training a machine learning model.
Given the dataset size and the requirement to optimize for GPU acceleration using NVIDIA technologies, which of the following is the most appropriate data processing library to use?

A. Modin with Ray backend
B. pandas
C. Dask DataFrame with Dask-CUDA
D. NumPy with CuPy acceleration

Answer: C

NEW QUESTION # 248
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