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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: GPU and Cloud Computing	16%	- GPU architecture and acceleration principles - Cloud GPU environments and deployment - CRISP-DM and data science methodology - Resource management and scaling strategies
Topic 2: Data Analysis	14%	- Exploratory Data Analysis (EDA) - Data visualization and graph analytics - Time-series analysis and anomaly detection - Distributed and parallel data processing
Topic 3: Data Manipulation and Software Literacy	19%	- Dependency management and containerization - Performance profiling and optimization tools - GPU-accelerated ETL workflows - Data processing libraries selection and usage
Topic 4: MLOps	19%	- Monitoring, logging and maintenance - End-to-end workflow management - Pipeline automation and orchestration - Model deployment and serving
Topic 5: Machine Learning	15%	- Model training and hyperparameter tuning - Model evaluation and validation - Distributed training strategies - GPU-accelerated ML frameworks and algorithms
Topic 6: Data Preparation	17%	- Data validation and quality assurance - Data cleaning, preprocessing and transformation - Feature engineering and data type optimization - Workflow monitoring and bottleneck identification

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q249-Q254):

NEW QUESTION # 249
You need to train a deep learning model using PyTorch on a dataset too large for a single GPU. You decide to use Dask with NVIDIA GPUs for multi-GPU scaling.
Which approach is the most effective for distributing the workload?

A. Use Dask.delayed to wrap PyTorch training functions and schedule them across multiple GPUs
B. Use Dask's built-in deep learning API to automatically distribute PyTorch models across GPUs
C. Use Dask Bag to shard the dataset and train separate PyTorch models on each shard
D. Use Dask-CUDA workers with PyTorch's DistributedDataParallel (DDP) for training across multiple GPUs

Answer: D

NEW QUESTION # 250
A machine learning team has deployed a fraud detection model in production, and they want to monitor its performance over time to detect model drift and performance degradation.
Which monitoring strategy is most effective for this use case?

A. Using a static dataset for monitoring instead of real-time production data
B. Implementing model performance tracking with drift detection metrics
C. Disabling logging to reduce system overhead and avoid storage costs
D. Logging only inference latency without tracking model predictions

Answer: B

NEW QUESTION # 251
You are working with a large dataset in a cloud environment for a deep learning model. The dataset consists of several features including numerical values, categorical data, and timestamps.
Which of the following choices would result in the most efficient use of GPU and cloud resources when determining the optimal data type for each feature? (Select three)

A. Use object data types for categorical features to avoid type conversion overhead.
B. Use int32 for all numerical features to save memory.
C. Use float64 for all numerical features to ensure maximum precision.
D. Use datetime64[ns] for timestamp features to ensure high precision.
E. Use int8 for categorical features where there are fewer than 256 categories.

Answer: B,D,E

NEW QUESTION # 252
You are processing a large dataset with UNIX timestamps (seconds since Jan 1, 1970) ranging from Jan 1, 2000, to the present.
What is the most memory-efficient data type for the timestamp column in a GPU-accelerated cloud environment?

A. df['timestamp'] = df['timestamp'].astype('int32')
B. df['timestamp'] = df['timestamp'].astype('datetime64[ms]')
C. df['timestamp'] = df['timestamp'].astype('int64')
D. df['timestamp'] = df['timestamp'].astype('float32')

Answer: A

NEW QUESTION # 253
What is the primary advantage of using NVIDIA Triton Inference Server for deploying and monitoring machine learning models in production?

A. It provides GPU optimization to handle high-throughput inference workloads.
B. It automatically tunes hyperparameters for all models.
C. It is designed solely for edge devices and not for data centers.
D. It only supports TensorFlow models for inference.

Answer: A

NEW QUESTION # 254
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