NCP-ADS Exam Quiz | NCP-ADS Reliable Test Sample

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

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100% Pass Quiz Newest NVIDIA - NCP-ADS - NVIDIA-Certified-Professional Accelerated Data Science Exam Quiz

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q110-Q115):

NEW QUESTION # 110
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. Reduce the number of GPUs used and focus on fine-tuning the hyperparameters for optimal performance on a single GPU.
C. Ensure efficient multi-GPU communication and synchronization strategies, such as using NCCL for distributed training.
D. Ensure that the model is distributed evenly across GPUs to prevent some GPUs from being underutilized.
E. Disable gradient accumulation when using multi-GPU setups to increase communication efficiency.

Answer: C,D

NEW QUESTION # 111
Which of the following methods are commonly used to handle missing data in data analysis? (Select two)

A. Using the mode to impute missing values
B. Imputing with the mean or median of the feature
C. Using a fixed constant value for imputation
D. Ignoring missing data entirely
E. Removing rows with missing data

Answer: B,E

NEW QUESTION # 112
You are working on a large-scale machine learning workload that involves training a deep learning model using multiple GPUs. You want to leverage Dask to implement data parallelism efficiently using NVIDIA GPUs.
Which of the following approaches best achieves data parallelism in this context?

A. Use Dask DataFrame to parallelize deep learning model training across multiple GPUs
B. Leverage Dask-CUDA to automatically assign computations to available GPUs using the worker pool
C. Run a single large Dask task on the CPU and use Dask-MPI for multi-GPU execution
D. Use Dask with CuPy to distribute NumPy-based computations across multiple GPUs

Answer: B

NEW QUESTION # 113
A data engineering team is designing an ETL pipeline to process large-scale financial transaction data. They want to leverage NVIDIA-accelerated ETL tools to extract data from a data lake, transform it by filtering and aggregating key fields, and load it into a data warehouse.
Which of the following approaches provides the most efficient ETL processing using NVIDIA technologies?

A. Use RAPIDS cuDF to preprocess data in-memory and BlazingSQL to accelerate SQL-based transformations
B. Use Dask on CPUs for distributed ETL processing and later move results to a GPU-based database
C. Perform all transformations using Pandas DataFrames before loading the data into the GPU
D. Write a custom ETL script in pure Python to handle data extraction, transformation, and loading

Answer: A

NEW QUESTION # 114
Which of the following best describes the functionality of DLProf in deep learning model profiling?

A. DLProf is used for visualizing model predictions, similar to TensorBoard.
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 uses statistical methods to predict future model performance based on historical data.

Answer: B

NEW QUESTION # 115
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