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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q182-Q187):

NEW QUESTION # 182
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 # 183
A data scientist is training a deep learning model on an NVIDIA GPU and wants to profile the model to identify performance bottlenecks. The scientist chooses to use NVIDIA DLProf.
Which of the following steps is the most effective way to profile the model using DLProf?

A. Run the model using dlprof --mode profile to collect performance metrics and generate a report.
B. Modify the training script to manually insert timing functions for each layer and compare execution times.
C. Use nvprof instead of DLProf since it provides more detailed profiling for deep learning workloads.
D. Rely on general CPU profiling tools like perf and gprof to analyze GPU performance.

Answer: A

NEW QUESTION # 184
Which of the following Nvidia technologies is primarily used for performing benchmarking and optimizing GPU-accelerated deep learning workflows, especially focusing on model training performance?

A. Nvidia DeepStream SDK
B. Nvidia Triton Inference Server
C. Nvidia Nsight Systems
D. Nvidia CUDA

Answer: C

NEW QUESTION # 185
A team of data scientists needs to deploy a machine learning model that depends on specific versions of CUDA and TensorFlow, ensuring it runs consistently across different machines without manually configuring each system.
Which of the following approaches best ensures consistency while leveraging NVIDIA GPUs?

A. Compiling all dependencies into the host machine and using system-wide installations
B. Using NVIDIA Docker (nvidia-docker) to containerize the model and manage GPU dependencies
C. Running the model in a local Python virtual environment and copying dependencies manually
D. Using Docker without GPU support and relying on CPU fallback when running TensorFlow

Answer: B

NEW QUESTION # 186
A data scientist is analyzing sales data for an e-commerce company that experiences strong seasonal trends (e.g., increased sales during holiday seasons). The goal is to accurately forecast future sales using GPU-accelerated data science techniques.
Which approach would be the most effective?

A. Compute a rolling average and manually adjust for seasonality based on previous peak sales months.
B. Train a Decision Tree model using cuML to classify future sales trends.
C. Apply k-Means clustering to identify seasonal patterns and extrapolate future values.
D. Use a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and optimize it using NVIDIA RAPIDS.

Answer: D

NEW QUESTION # 187
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