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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - Data loading and preprocessing 1. NVIDIA DALI for high-performance data loading 2. Handling class imbalance and generating synthetic data - Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables
Topic 2: GPU and Cloud Computing	16%	- GPU resource management 1. Efficient GPU resource allocation and scheduling - GPU architecture and fundamentals 1. GPU architecture fundamentals for data science 2. CPU vs GPU workloads and memory transfer optimization - Performance optimization 1. Mixed precision and bottleneck analysis 2. Single and multi-GPU performance optimization 3. Memory profiling with DLProf - Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud
Topic 3: MLOps	19%	- Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows - Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools - Model deployment and serving 1. Production deployment strategies 2. Model saving, loading, and prediction generation
Topic 4: Data Manipulation and Software Literacy	19%	- Distributed computing with Dask 1. Dask-cuDF for parallel data processing 2. Scaling data operations across multiple GPUs - Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency - GPU-accelerated data manipulation using cuDF 1. Data integration, joining, merging, and filtering 2. cuDF vs pandas API mapping and usage 3. Groupby, apply, and aggregation operations
Topic 5: Data Analysis	14%	- Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting
Topic 6: Machine Learning	15%	- Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch - Feature engineering and hyperparameter tuning 1. Hyperparameter tuning techniques 2. Batching and memory-efficient training methods 3. Feature engineering for ML models - Model training with GPU acceleration 1. Multi-GPU training strategies 2. Selection of appropriate algorithms for GPU execution 3. Training models using cuML and GPU-accelerated XGBoost

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q239-Q244):

NEW QUESTION # 239
You are tasked with implementing a multi-GPU data pipeline using Dask-CUDA to process large datasets stored in Parquet format. Your goal is to achieve optimal GPU memory utilization and minimize inter-GPU communication overhead.
Which of the following approaches best aligns with these goals?

A. Set dask.config.set({'distributed.worker.memory.target': 0.9}) to allocate 90% of the total CPU memory for GPU operations.
B. Use dask_cudf.read_parquet() with split_row_groups=True to evenly distribute data across GPUs.
C. Use dask.array instead of dask_cudf because it provides better performance for structured tabular data.
D. Use dask.persist() instead of dask.compute() to force immediate execution of tasks before distribution to GPUs.

Answer: B

NEW QUESTION # 240
You are tasked with optimizing the performance of a large-scale data science project that involves deep learning models on a cloud infrastructure. Your organization is using GPUs for model training.
Which of the following strategies would be the most effective in optimizing GPU performance for data science tasks? (Select two)

A. Optimize GPU performance by limiting the number of threads running on each GPU.
B. Utilize multi-GPU training to parallelize the workload, reducing training time.
C. Overclock the GPU to achieve higher computational speeds and improve training times.
D. Use larger batch sizes to make better use of GPU memory during model training.
E. Use a single cloud instance with the largest GPU available to ensure maximum performance.

Answer: B,D

NEW QUESTION # 241
A data scientist wants to process a large dataset using multiple GPUs on an NVIDIA-supported system. They decide to use Dask to enable parallelism.
Which of the following steps is most essential for leveraging Dask for multi-GPU scaling?

A. Use dask.array or dask.dataframe with dask_cuda.CUDACluster to distribute computations across multiple GPUs
B. Use ThreadPoolExecutor to manage parallel computations on GPUs
C. Train a deep learning model on a single GPU first, then switch to Dask for scaling
D. Convert all data into Pandas DataFrames before distributing computations

Answer: A

NEW QUESTION # 242
You are analyzing a transportation network where airports represent nodes and flight routes represent edges. You need to determine the most critical airports in the network based on how many shortest paths pass through them.
Which cuGraph centrality algorithm should you use for this task?

A. Closeness Centrality, as it measures how close an airport is to all other airports, making it the best indicator of global connectivity.
B. Betweenness Centrality, as it identifies airports that act as major transit hubs by measuring how frequently they appear in shortest paths.
C. Degree Centrality, as it measures the number of direct connections an airport has, indicating its importance in the network.
D. Triangle Count, as it determines the number of three-node cycles an airport is part of, indicating its importance in network structure.

Answer: B

NEW QUESTION # 243
You have trained a machine learning model using cuML as part of the Modeling phase in the CRISP- DM framework. Now, you need to assess how well the model performs before moving forward with deployment.
Which of the following steps aligns best with the Evaluation phase of CRISP-DM using NVIDIA technologies?

A. Define the problem statement and collect relevant datasets before training the model.
B. Deploy the model to an edge device using TensorRT for real-time inference.
C. Compute model accuracy, precision, and recall using cuml.metrics.accuracy_score() and cuml.metrics.classification_report().
D. Optimize the data pipeline using cudf.DataFrame.merge() to improve data loading speed.

Answer: C

NEW QUESTION # 244
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