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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Benchmarking GPU workflows 2. CRISP-DM workflow execution 3. Docker and Conda environment management
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
Machine Learning	15%	- Model Development and Optimization 1. Multi-GPU training comparison 2. Memory optimization techniques (mixed precision, batching) 3. Hyperparameter tuning 4. Feature engineering
Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Perform time series analysis and visualization 2. Detect anomalies in time series datasets 3. Use cuGraph for graph analytics
Data Preparation	17%	- Data Cleaning and Transformation 1. Synthetic data generation with RAPIDS 2. cuDF and pandas data preprocessing 3. Data normalization and standardization

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q28-Q33):

NEW QUESTION # 28
You are preparing a dataset for training a machine learning model using NVIDIA RAPIDS cuML. The dataset contains a feature representing timestamps in nanoseconds.
To optimize GPU performance while ensuring precision, which data type should you choose?

A. int32 - Uses less memory and can store high-precision timestamps efficiently.
B. datetime64[ns] - Optimizes storage and computation for timestamp data in RAPIDS.
C. object - Allows flexibility in storing timestamps as strings for easier parsing.
D. bool - Provides a highly efficient way to store timestamps as binary values.

Answer: B

NEW QUESTION # 29
You are tasked with optimizing the performance of an MLOps pipeline that uses GPU-accelerated workflows. After running initial benchmarks, you notice that the training time is higher than expected, despite the use of multiple GPUs.
What are the best strategies to optimize the GPU-accelerated workflow in this case? (Select two)

A. Increase the batch size to better utilize the multiple GPUs and reduce the number of updates to the model during training.
B. Disable gradient accumulation when using multi-GPU setups to increase communication efficiency.
C. Ensure that the model is distributed evenly across GPUs to prevent some GPUs from being underutilized.
D. Reduce the number of GPUs used and focus on fine-tuning the hyperparameters for optimal performance on a single GPU.
E. Ensure efficient multi-GPU communication and synchronization strategies, such as using NCCL for distributed training.

Answer: C,E

NEW QUESTION # 30
You are a data scientist analyzing a social media network with NVIDIA cuGraph to identify the most influential users using the PageRank algorithm.
Which option best describes how cuGraph PageRank operates on a directed graph?

A. PageRank in cuGraph operates only on undirected graphs and cannot be applied to networks where edges have a direction.
B. PageRank assigns equal importance to all nodes in the graph initially and updates values only based on outgoing edges, ignoring incoming edges.
C. PageRank in cuGraph is a label propagation algorithm that clusters nodes into communities rather than ranking their importance.
D. PageRank in cuGraph uses an iterative power method to update node importance values based on incoming edges, incorporating a damping factor to handle random jumps.

Answer: D

NEW QUESTION # 31
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 operations are executed lazily and require an explicit compute() call to execute transformations.
B. cuDF supports common Pandas-like operations, such as filtering, merging, and groupby, but performs them efficiently on the GPU.
C. cuDF can be used as a direct replacement for Pandas, but it is optimized for multi-node distributed computing rather than GPU acceleration.
D. cuDF requires all input data to be stored in Apache Parquet format, as it does not support CSV or JSON ingestion.

Answer: B

NEW QUESTION # 32
Which of the following best describes the purpose of the NVIDIA TensorRT library?

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

Answer: A

NEW QUESTION # 33
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