New NVIDIA NCP-ADS Study Materials - NCP-ADS Real Exams

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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
Topic 2: MLOps	19%	- Deployment and Monitoring 1. Model deployment in production environments 2. Memory and capacity evaluation 3. Performance benchmarking and optimization
Topic 3: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. GPU-accelerated ETL design and implementation 2. Distributed data processing frameworks (Dask) 3. Data caching and performance optimization
Topic 4: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. CRISP-DM workflow execution 2. Benchmarking GPU workflows 3. Docker and Conda environment management
Topic 5: Data Preparation	17%	- Data Cleaning and Transformation 1. Synthetic data generation with RAPIDS 2. cuDF and pandas data preprocessing 3. Data normalization and standardization
Topic 6: Machine Learning	15%	- Model Development and Optimization 1. Multi-GPU training comparison 2. Memory optimization techniques (mixed precision, batching) 3. Feature engineering 4. Hyperparameter tuning

>> New NVIDIA NCP-ADS Study Materials <<

NCP-ADS Real Exams | NCP-ADS Exam Syllabus

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q102-Q107):

NEW QUESTION # 102
You are training a deep learning model for image classification and want to optimize its hyperparameters, including learning rate, batch size, and number of layers.
Which of the following techniques is the most effective for efficiently searching through a high- dimensional hyperparameter space?

A. Bayesian Optimization
B. Gradient Descent
C. Grid Search
D. Random Search

Answer: A

NEW QUESTION # 103
You are processing a multi-terabyte dataset in CuDF and want to optimize query performance and storage efficiency.
Which approach should you follow to ensure that the dataset remains efficiently partitioned and easily accessible?

A. Convert all numeric columns to float64 for higher precision, even if float32 is sufficient.
B. Store the entire dataset in a single large file to minimize the number of files in the directory.
C. Split the dataset into multiple files based on a logical partitioning key, such as a date column.
D. Use pandas for large-scale processing instead of CuDF since pandas is more stable for big data processing.

Answer: C

NEW QUESTION # 104
A data scientist is working with large-scale ETL (Extract, Transform, Load) pipelines on GPU- accelerated infrastructure using RAPIDS. The workload involves frequent shuffle operations, which significantly impact performance.
What is the best approach using NVIDIA technologies to reduce shuffle overhead and improve performance?

A. Use RAPIDS cuDF's GPU memory caching to store intermediate DataFrames and avoid redundant shuffle operations.
B. Use RAPIDS cuML to replace shuffle-intensive operations with an ML model that predicts data distribution.
C. Enable CUDA Unified Memory to automatically optimize shuffle performance without manual intervention.
D. Store all intermediate shuffle data in CPU memory using pandas to ensure persistence and reduce GPU load.

Answer: A

NEW QUESTION # 105
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. Business Understanding
B. Data Understanding
C. Data Preparation
D. Model Building

Answer: A

NEW QUESTION # 106
Which of the following hardware components is most critical for accelerating the training of deep learning models?

A. Random Access Memory (RAM)
B. Graphics Processing Unit (GPU)
C. Central Processing Unit (CPU)
D. Solid State Drive (SSD)

Answer: B

NEW QUESTION # 107
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