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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q189-Q194):

NEW QUESTION # 189
You are working with a large dataset containing 500 million records stored as a parquet file. Your task is to perform data filtering, aggregation, and transformation as efficiently as possible.
Which of the following approaches would be the best choice for accelerated data manipulation using NVIDIA technologies?

A. Use multiprocessing in Python to parallelize pandas operations across CPU cores
B. Convert the dataset to a NumPy array and use standard Python loops
C. Load the dataset into pandas and use apply() for transformations
D. Use RAPIDS cuDF to load, filter, and transform the dataset on the GPU

Answer: D

NEW QUESTION # 190
You are working on a large-scale machine learning pipeline that involves processing massive datasets using multiple GPUs on an NVIDIA DGX system. You choose to use Dask to enable efficient parallel processing across multiple GPUs.
Which of the following steps is essential to correctly configure Dask for multi-GPU acceleration?

A. Use dask.distributed.Client() without specifying a scheduler to automatically detect available GPUs.
B. Assign computation tasks explicitly to CPUs using dask.config.set({'scheduler': 'threads'}) before using GPUs.
C. Use dask_cuda.LocalCUDACluster() to create a cluster of GPU workers and pass it to the Dask client.
D. Avoid using dask_cudf and instead rely on standard pandas DataFrames to ensure GPU-accelerated execution.

Answer: C

NEW QUESTION # 191
You are analyzing a dataset that contains missing values.
Which of the following techniques is most appropriate when dealing with missing numerical data in a dataset, ensuring minimal impact on model performance?

A. Using k-nearest neighbors (KNN) imputation
B. Replacing missing values with a constant value (e.g., zero)
C. Replacing missing values with the mean of the column
D. Removing rows with missing values

Answer: A

NEW QUESTION # 192
You are designing a data pipeline for a healthcare analytics company that processes petabytes of structured and semi-structured patient data. Your goal is to optimize performance while maintaining accuracy.
Which of the following strategies is the most appropriate choice for accelerating this workload?

A. Use RAPIDS cuDF and Dask-cuDF for distributed preprocessing and RAPIDS cuML for GPU- accelerated machine learning model training.
B. Use RAPIDS cuDF for data ingestion, preprocessing, and transformation, then offload model training to scikit-learn on CPUs.
C. Load the data into a PostgreSQL database and run SQL queries for all transformations before training a model.
D. Use Apache Hadoop and MapReduce for preprocessing before training models on a single GPU.

Answer: A

NEW QUESTION # 193
You are processing large-scale datasets in Dask-cuDF and observe that your computation involves excessive data shuffling, which slows down performance. You decide to implement data caching to reduce shuffle overhead.
Which of the following best describes a technique to reduce shuffle costs in a Dask-cuDF workflow?

A. Persisting intermediate results in distributed GPU memory using df.persist() ensures that Dask does not recompute partitions, reducing shuffle overhead.
B. Using .compute() on each partition immediately forces Dask to materialize results and store them in memory, ensuring shuffle operations are avoided.
C. Disabling lazy execution using dask.config.set(lazy=False) forces all computations to execute eagerly, thereby avoiding shuffle.
D. Splitting the DataFrame into multiple smaller DataFrames and recomputing each separately reduces shuffle operations in Dask-cuDF.

Answer: A

NEW QUESTION # 194
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