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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q16-Q21):

NEW QUESTION # 16
A machine learning engineer is training a convolutional neural network (CNN) on an NVIDIA GPU and needs to maximize throughput while avoiding OOM errors.
Which of the following techniques is the most effective way to balance memory efficiency and training speed?

A. Using dynamic batch sizing based on available GPU memory
B. Loading all dataset samples into GPU memory at the start of training
C. Allocating a fixed batch size without monitoring memory usage
D. Using a batch size of 1 to minimize memory usage

Answer: A

NEW QUESTION # 17
Your data science team is performing exploratory data analysis (EDA) on a large GPU-accelerated environment using cuDF and Dask-cuDF. During analysis, queries on categorical columns are performing poorly.
Which approach will most effectively improve query performance for categorical data in GPU-accelerated DataFrames?

A. Use CPU-based DataFrames (pandas) instead of GPU-based DataFrames
B. Convert categorical columns to integer codes
C. Increase GPU memory allocation
D. Store data in compressed file formats like Parquet

Answer: B

NEW QUESTION # 18
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. Ensure that the model is distributed evenly across GPUs to prevent some GPUs from being underutilized.
B. Disable gradient accumulation when using multi-GPU setups to increase communication efficiency.
C. Reduce the number of GPUs used and focus on fine-tuning the hyperparameters for optimal performance on a single GPU.
D. Increase the batch size to better utilize the multiple GPUs and reduce the number of updates to the model during training.
E. Ensure efficient multi-GPU communication and synchronization strategies, such as using NCCL for distributed training.

Answer: A,E

NEW QUESTION # 19
You are using cuGraph to run the PageRank algorithm on a directed web graph. The dataset is large, and you want to ensure an accurate and efficient computation while optimizing GPU performance.
Which of the following configurations is the best approach for running PageRank in cuGraph?

A. Convert the graph into an adjacency matrix and perform matrix multiplication iteratively for convergence
B. Run cugraph.pagerank() with a damping factor of 0.85 and set the max iterations to 100 with a convergence threshold
C. Use the cugraph.pagerank() function with a damping factor of 0 and 10 iterations
D. Load the graph into NetworkX first, compute PageRank, and then convert the results back into cuGraph format

Answer: B

NEW QUESTION # 20
Which of the following Nvidia technologies is commonly used for deploying machine learning models in production environments, enabling scalable deployment and monitoring?

A. Nvidia Triton Inference Server
B. Nvidia DeepStream
C. Nvidia CUDA
D. Nvidia Jetson Nano

Answer: A

NEW QUESTION # 21
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