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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q219-Q224):

NEW QUESTION # 219
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 # 220
You are building an MLOps pipeline for a predictive model that uses tabular data with both categorical and numerical features.
To ensure efficient data processing and optimal model training on an NVIDIA GPU, which of the following data types would be most suitable for a categorical feature representing different product categories?

A. Int32
B. String
C. Float64
D. Int64

Answer: A

NEW QUESTION # 221
You are working with a dataset containing hundreds of millions of records, and you need to perform ETL operations such as filtering, joins, and aggregations. Given the dataset size, which NVIDIA- accelerated library should you use to achieve optimal performance?

A. cuDF, as it provides GPU-accelerated DataFrame operations similar to Pandas, allowing for efficient processing of large datasets.
B. NumPy, because it is optimized for numerical computing and offers better performance for handling tabular data.
C. Pandas, as it is widely used and supports all common DataFrame operations, even for very large datasets.
D. cuPy, because it provides GPU-accelerated array operations, making it the best option for processing tabular data.

Answer: A

NEW QUESTION # 222
You are running a data science project on a cloud environment, where you need to optimize the GPU utilization for real-time data processing tasks.
Which of the following practices should you consider to maximize GPU performance? (Select two)

A. Scale the cloud infrastructure vertically by increasing the size of the GPU in use.
B. Use NVIDIA CUDA libraries optimized for data science tasks to accelerate computations.
C. Prioritize using CPU for computationally expensive tasks and reserve GPU for I/O operations.
D. Use data parallelism techniques to split the workload evenly across multiple GPUs.
E. Implement model inference in a batch mode rather than in a real-time streaming mode for better performance.

Answer: B,D

NEW QUESTION # 223
Which feature of NVIDIA MLFlow integration with Triton Inference Server allows for the seamless deployment and monitoring of models in production?

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

Answer: D

NEW QUESTION # 224
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