NCP-ADS exam collection guarantee NCP-ADS NVIDIA-Certified-Professional Accelerated Data Science exam success

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
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
GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Docker and Conda environment management 2. CRISP-DM workflow execution 3. Benchmarking GPU workflows
MLOps	19%	- Deployment and Monitoring 1. Model deployment in production environments 2. Memory and capacity evaluation 3. Performance benchmarking and optimization
Data Preparation	17%	- Data Cleaning and Transformation 1. cuDF and pandas data preprocessing 2. Synthetic data generation with RAPIDS 3. Data normalization and standardization
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 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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q224-Q229):

NEW QUESTION # 224
Which of the following best describes the functionality of DLProf in deep learning model profiling?

A. DLProf uses statistical methods to predict future model performance based on historical data.
B. DLProf tracks GPU utilization, memory bandwidth, and kernel execution time to identify performance bottlenecks.
C. DLProf can only profile the CPU side of a deep learning model's performance.
D. DLProf is used for visualizing model predictions, similar to TensorBoard.

Answer: B

NEW QUESTION # 225
A data scientist is training a deep learning model on an NVIDIA GPU but notices that the training speed is not significantly faster than when using a CPU.
Which of the following strategies is the best approach to fully utilize GPU acceleration and optimize training performance?

A. Increasing the batch size beyond the GPU's memory limit
B. Using only CPU-based data augmentation to keep the GPU dedicated to training
C. Using mixed precision training with NVIDIA Tensor Cores
D. Disabling cuDNN optimizations to allow more flexibility in kernel execution

Answer: C

NEW QUESTION # 226
Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

A. Z-score Standardization
B. Robust Scaling
C. Log Transformation
D. Min-Max Scaling

Answer: B

NEW QUESTION # 227
When scaling data parallelism using Dask with multiple Nvidia GPUs, what is the key consideration to avoid memory issues when distributing large datasets?

A. Ensure that each GPU's memory usage is manually monitored and adjusted, as Dask does not manage memory allocation automatically across GPUs.
B. Use dask_gpu instead of dask_cuda to manage memory automatically across GPUs.
C. Allow Dask to allocate data chunks dynamically without partitioning the dataset first, letting the system handle memory distribution automatically.
D. Split the dataset into smaller partitions that fit into each GPU's memory to prevent out-of-memory errors, and let Dask manage data distribution.

Answer: D

NEW QUESTION # 228
Which of the following steps is the first in the CRISP-DM (Cross-Industry Standard Process for Data Mining) process when using NVIDIA technologies?

A. Model Building
B. Business Understanding
C. Data Preparation
D. Data Understanding

Answer: B

NEW QUESTION # 229
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