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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q257-Q262):

NEW QUESTION # 257
A machine learning engineer wants to evaluate the performance of NVIDIA RAPIDS cuDF and Apache Spark for large-scale data processing on a GPU-enabled cluster.
Which of the following strategies is the most effective for obtaining a fair and comprehensive benchmark?

A. Limit the benchmark to small datasets since GPUs excel at parallel processing.
B. Execute identical ETL workflows on cuDF and Spark-RAPIDS and measure execution time and resource utilization.
C. Focus only on processing speed without considering resource consumption differences between frameworks.
D. Run Spark on a CPU cluster while running RAPIDS on a GPU to compare real-world scenarios.

Answer: B

NEW QUESTION # 258
You are working with a cuDF DataFrame and need to convert a column named sales from float64 to int32 to save memory.
Which of the following is the correct and most efficient way to perform this conversion in cuDF?

A. df['sales'] = df['sales'].to_numeric('int32')
B. df['sales'].convert_dtypes('int32')
C. df['sales'] = df['sales'].astype('int32')
D. df['sales'].apply(lambda x: int(x))

Answer: C

NEW QUESTION # 259
You are working with a large dataset using NVIDIA RAPIDS cuDF and need to normalize a numerical column (price) to scale its values between 0 and 1.
Which of the following approaches correctly normalizes the column using cuDF?

A. df["price"] = df["price"].applymap( 2. lambda x: (x - df["price"].min()) 3. / (df["price"].max() - df["price"].min()) 4. )
B. df["price"] = df["price"] / df["price"].max()
C. df["price"] = (df["price"] - df["price"].mean()) / df["price"].std()
D. df["price"] = ( 2. df["price"] - df["price"].min() 3. ) / (df["price"].max() - df["price"].min())

Answer: D

NEW QUESTION # 260
You are tasked with optimizing the performance of an MLOps pipeline that uses GPU-accelerated workflows. After running initial benchmarks, you notice that the training time is higher than expected, despite the use of multiple GPUs.
What are the best strategies to optimize the GPU-accelerated workflow in this case? (Select two)

A. Ensure efficient multi-GPU communication and synchronization strategies, such as using NCCL for distributed training.
B. Increase the batch size to better utilize the multiple GPUs and reduce the number of updates to the model during training.
C. Reduce the number of GPUs used and focus on fine-tuning the hyperparameters for optimal performance on a single GPU.
D. Disable gradient accumulation when using multi-GPU setups to increase communication efficiency.
E. Ensure that the model is distributed evenly across GPUs to prevent some GPUs from being underutilized.

Answer: A,E

NEW QUESTION # 261
You are working on a machine learning problem that involves training a deep learning model on a dataset with billions of records. The dataset is stored in a distributed cloud storage system.
Given the need for acceleration, which is the most effective approach?

A. Use GPU acceleration with libraries like RAPIDS AI or TensorFlow to leverage parallel processing.
B. Load the entire dataset into RAM on a single powerful CPU-based machine before starting model training.
C. Reduce the dataset to a small representative sample to avoid the need for specialized acceleration.
D. Store the dataset in a relational database and query it sequentially using SQL before training the model.

Answer: A

NEW QUESTION # 262
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