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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q288-Q293):

NEW QUESTION # 288
You are comparing the performance of NVIDIA RAPIDS cuML, TensorFlow, and PyTorch for training and inference on a dataset with millions of records.
To design a fair and effective benchmark, which approach should you take?

A. Run each framework on different GPUs to maximize available resources and compare execution times across different hardware configurations.
B. Ensure all frameworks run on the same GPU, use optimized batch sizes, and measure execution time and memory usage with NVIDIA Nsight Systems.
C. Use only a CPU baseline for comparison to demonstrate the benefits of GPU acceleration, ignoring GPU-specific optimizations.

Answer: B

NEW QUESTION # 289
A data scientist is working on training a deep learning model in a cloud-based environment. The dataset is large, and model convergence is taking too long on a standard CPU instance.
To optimize performance through GPU acceleration, which of the following strategies should the data scientist implement?

A. Use a cloud instance with multiple GPUs and enable mixed-precision training.
B. Disable CUDA and use only OpenMP to parallelize computations across CPU cores.
C. Increase the number of CPU cores and distribute training across multiple CPU threads.
D. Store all training data in RAM and load it directly to the CPU for processing.

Answer: A

NEW QUESTION # 290
Which tools or technologies from NVIDIA are essential for implementing an efficient MLOps pipeline in production environments? (Select two)

A. NVIDIA TensorRT for efficient model inference
B. NVIDIA Triton Inference Server for managing deployment and serving models
C. NVIDIA NGC for storing and sharing machine learning datasets
D. NVIDIA CUDA for model training in cloud environments
E. NVIDIA DLA (Deep Learning Accelerator) for model deployment

Answer: A,B

NEW QUESTION # 291
A data scientist is preprocessing a dataset containing multiple categorical features using NVIDIA RAPIDS to accelerate feature engineering.
The dataset contains:
A low-cardinality categorical feature (Product Type) with 10 unique values.
A high-cardinality categorical feature (User ID) with 100,000 unique values.
A numerical feature (Price) that requires transformation.
Which of the following feature engineering approaches will be the most efficient for GPU acceleration?

A. Convert Product Type to integers using label encoding, use frequency encoding for User ID, and normalize Price using float32.
B. Using float32 for Price is optimal for GPU-based ML models, balancing precision and computational efficiency.
C. Convert both Product Type and User ID to int64 and use standardization (mean normalization) on Price.
D. Frequency encoding for User ID is an efficient alternative to one-hot encoding, as it replaces each category with its frequency in the dataset, reducing dimensionality while preserving useful information.
E. Apply one-hot encoding to both Product Type and User ID, and scale Price using float64 precision.
F. Store both Product Type and User ID as string data types in cuDF to maintain raw categorical information.

Answer: A

NEW QUESTION # 292
You are training a deep learning model on a large dataset. Initially, you train the model on a single GPU and achieve a training time of 10 hours. To speed up training, you switch to a multi-GPU setup with four GPUs. However, after testing, you notice that the training time is only reduced to 3.5 hours instead of the expected 2.5 hours (a linear speedup).
What is the most likely reason for this sublinear speedup?

A. The optimizer is not designed for multi-GPU training, causing inefficiency.
B. Increased memory bandwidth usage causes a bottleneck in the system.
C. The learning rate is too low, causing slower convergence despite multiple GPUs.
D. Data transfer and communication overhead between GPUs limit scalability.

Answer: D

NEW QUESTION # 293
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