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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Perform time series analysis and visualization 2. Use cuGraph for graph analytics 3. Detect anomalies in time series datasets
Topic 2: MLOps	19%	- Deployment and Monitoring 1. Performance benchmarking and optimization 2. Model deployment in production environments 3. Memory and capacity evaluation
Topic 3: GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Benchmarking GPU workflows 2. CRISP-DM workflow execution 3. Docker and Conda environment management
Topic 4: Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. Synthetic data generation with RAPIDS 3. cuDF and pandas data preprocessing
Topic 5: Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. GPU-accelerated ETL design and implementation 2. Data caching and performance optimization 3. Distributed data processing frameworks (Dask)
Topic 6: Machine Learning	15%	- Model Development and Optimization 1. Hyperparameter tuning 2. Multi-GPU training comparison 3. Memory optimization techniques (mixed precision, batching) 4. Feature engineering

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q262-Q267):

NEW QUESTION # 262
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 float64 for all numerical features to ensure maximum precision.
B. Use int32 for all numerical features to save memory.
C. Use object data types for categorical features to avoid type conversion overhead.
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 # 263
You need to determine the optimal data processing library for a small dataset of 500,000 records that will be processed on a multi-core CPU machine with no GPU access.
Which of the following libraries would be the most efficient for this task?

A. Dask
B. pandas
C. cuDF
D. PyTorch

Answer: B

NEW QUESTION # 264
You are working with a deep learning model for real-time inference on an NVIDIA GPU. Your objective is to optimize inference speed while maintaining acceptable model accuracy.
Which of the following techniques provides the best balance between inference performance and accuracy?

A. Use double-precision (FP64) computations to prevent numerical instability in inference
B. Disable batch inference to ensure each sample is processed independently for better accuracy
C. Increase the number of neurons in each layer to improve the model's accuracy
D. Use TensorRT for model optimization, including precision quantization and layer fusion

Answer: D

NEW QUESTION # 265
Which of the following techniques are best suited for efficiently processing and organizing large datasets using NVIDIA technologies? (Select two)

A. Storing data directly on an HDD and processing with GPUs
B. Leveraging Dask for distributed GPU-based parallel computing
C. Using TensorFlow with custom data pipelines for data loading
D. Using cuDF for GPU-accelerated DataFrame operations

Answer: B,D

NEW QUESTION # 266
Which feature of NVIDIA MLFlow integration with Triton Inference Server allows for the seamless deployment and monitoring of models in production?

A. MLFlow allows for version control and automated model rollout.
B. MLFlow prevents models from being updated once deployed to production.
C. MLFlow only supports deployment on CPUs.
D. MLFlow handles only the training and not the deployment of models.

Answer: A

NEW QUESTION # 267
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