New NCP-ADS Exam Format - Printable NCP-ADS PDF

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

>> New NCP-ADS Exam Format <<

100% Free NCP-ADS – 100% Free New Exam Format | High Hit-Rate Printable NVIDIA-Certified-Professional Accelerated Data Science PDF

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q213-Q218):

NEW QUESTION # 213
A machine learning engineer wants to accelerate the training of a large deep learning model while optimizing GPU utilization.
Which of the following strategies provides the best performance improvement using NVIDIA technologies?

A. Disable mixed-precision training since it reduces numerical accuracy and does not significantly impact performance.
B. Load all training data into GPU memory at once to minimize CPU-GPU data transfer time.
C. Use NVIDIA DALI (Data Loading Library) to preprocess and augment data on the GPU before feeding it to the model.
D. Use CPU-based data augmentation techniques before transferring the data to the GPU for training.

Answer: C

NEW QUESTION # 214
You are using NVIDIA DLProf to analyze the performance of a deep learning model deployed on an A100 GPU. The report indicates that compute-bound operations are dominating execution time, and kernel execution efficiency is below 50%.
What is the best action to take based on this insight?

A. Use DLProf's Tensor Core Analysis to check if the model is leveraging Tensor Cores effectively.
B. Enable mixed precision training to improve computational efficiency.
C. Increase the batch size to fully utilize available GPU memory and reduce per-sample processing overhead.
D. Reduce the number of layers in the model to decrease computation time.

Answer: A

NEW QUESTION # 215
Which of the following best describes a key advantage of using cloud-based GPU instances for machine learning model training?

A. Cloud GPUs are always more cost-effective than on-premise GPUs, as they do not incur long-term usage costs.
B. Cloud-based GPU instances offer lower latency and better network performance compared to on- premise deployments, regardless of geographical location.
C. Cloud GPU instances cannot support containerized workloads, limiting their applicability for MLOps and CI/CD pipelines.
D. Cloud GPUs provide dynamically scalable resources, allowing users to increase or decrease compute power based on demand without upfront hardware investment.

Answer: D

NEW QUESTION # 216
Which of the following actions can you perform using DLProf to analyze a deep learning model's performance?

A. Automatically adjust the learning rate based on the model's convergence
B. Modify the training dataset during model execution
C. Increase batch size to improve accuracy
D. Visualize GPU memory utilization over time

Answer: D

NEW QUESTION # 217
You are working on an MLOps workflow that loads a dataset into GPU memory for model training using RAPIDS cuDF. Before performing transformations, you want to verify that the dataset will fit into available GPU memory.
Which of the following methods provides the most accurate estimate of dataset memory consumption in a RAPIDS cudf.DataFrame?

A. cudf_df.memory_usage().sum()
B. cudf_df.memory_usage(deep=True).sum()
C. cudf_df.to_pandas().memory_usage(deep=True).sum()
D. cudf_df.__sizeof__()

Answer: A

NEW QUESTION # 218
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