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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. Docker and Conda environment management 3. CRISP-DM workflow execution
MLOps	19%	- Deployment and Monitoring 1. Performance benchmarking and optimization 2. Memory and capacity evaluation 3. Model deployment in production environments
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 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)
Data Preparation	17%	- Data Cleaning and Transformation 1. Synthetic data generation with RAPIDS 2. cuDF and pandas data preprocessing 3. Data normalization and standardization
Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Use cuGraph for graph analytics 2. Detect anomalies in time series datasets 3. Perform time series analysis and visualization

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q251-Q256):

NEW QUESTION # 251
You are tasked with designing a benchmark to compare the performance of different GPU- accelerated machine learning frameworks, such as TensorFlow, PyTorch, and RAPIDS.
Which of the following factors is the most critical to ensure a fair and meaningful comparison?

A. Using the latest software versions of each framework, regardless of compatibility with the GPU hardware.
B. Ensuring all frameworks use the same dataset, batch size, and GPU hardware during benchmarking.
C. Allowing each framework to run with its default settings, as tuning hyperparameters would bias the results.
D. Using different model architectures per framework to test a variety of scenarios and ensure a broad evaluation.

Answer: B

NEW QUESTION # 252
You are using RAPIDS and Dask-cuDF to process a large-scale ETL pipeline. The workflow involves multiple join and groupby operations, which are causing excessive shuffling.
How can you best optimize caching to reduce shuffle overhead?

A. Use dask.persist() to store frequently accessed cuDF DataFrames in GPU memory, reducing recomputation and shuffle operations.
B. Cache data using Apache Arrow's in-memory format, but process all operations on CPU.
C. Force every operation to be recomputed from the raw dataset to ensure accurate results.
D. Split the dataset into multiple smaller Pandas DataFrames and store them in memory for quick retrieval.

Answer: A

NEW QUESTION # 253
You are working with a cuDF DataFrame and need to convert a column named sales from float64 to int32 to save memory.
Which of the following is the correct and most efficient way to perform this conversion in cuDF?

A. df['sales'].convert_dtypes('int32')
B. df['sales'] = df['sales'].astype('int32')
C. df['sales'] = df['sales'].to_numeric('int32')
D. df['sales'].apply(lambda x: int(x))

Answer: B

NEW QUESTION # 254
A machine learning engineer runs NVIDIA DLProf to analyze the performance of a deep learning model and receives a report indicating high GPU idle time.
What is the most likely cause of this issue?

A. The model is experiencing data loading bottlenecks, causing the GPU to wait for input batches.
B. The CUDA cores are overheating, leading to automatic throttling of computations.
C. The GPU is not powerful enough to process the deep learning model efficiently.
D. The batch size is too large, leading to excessive GPU memory utilization and slow processing.

Answer: A

NEW QUESTION # 255
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 efficient multi-GPU communication and synchronization strategies, such as using NCCL for distributed training.
B. Increase the batch size to better utilize the multiple GPUs and reduce the number of updates to the model during training.
C. Disable gradient accumulation when using multi-GPU setups to increase communication efficiency.
D. Ensure that the model is distributed evenly across GPUs to prevent some GPUs from being underutilized.
E. Reduce the number of GPUs used and focus on fine-tuning the hyperparameters for optimal performance on a single GPU.

Answer: A,D

NEW QUESTION # 256
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