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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q194-Q199):

NEW QUESTION # 194
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. Using .compute() on each partition immediately forces Dask to materialize results and store them in memory, ensuring shuffle operations are avoided.
B. Splitting the DataFrame into multiple smaller DataFrames and recomputing each separately reduces shuffle operations in Dask-cuDF.
C. Disabling lazy execution using dask.config.set(lazy=False) forces all computations to execute eagerly, thereby avoiding shuffle.
D. Persisting intermediate results in distributed GPU memory using df.persist() ensures that Dask does not recompute partitions, reducing shuffle overhead.

Answer: D

NEW QUESTION # 195
You are working on a structured dataset of around 10GB and need to perform exploratory data analysis (EDA), feature engineering, and filtering operations efficiently using NVIDIA technologies. The dataset fits into a single GPU's memory.
Which data processing library should you use to achieve the best performance?

A. Dask DataFrame with Dask-CUDA
B. cuDF
C. pandas
D. Spark with RAPIDS Accelerator

Answer: B

NEW QUESTION # 196
You are consulting for a retail company that collects data from daily sales transactions, customer interactions, and inventory tracking across multiple locations. They are unsure whether their dataset qualifies as big data and which processing method would be most suitable.
Which of the following characteristics best indicate that the dataset requires big data processing and acceleration techniques?

A. The dataset exceeds the memory capacity of a single machine and requires distributed or GPU- accelerated processing
B. The dataset contains more than 1 million records, making it impossible to process using pandas
C. The dataset includes a mix of numerical and categorical variables, requiring additional preprocessing
D. The dataset is stored in multiple relational database tables, making querying inefficient

Answer: A

NEW QUESTION # 197
You are a data scientist working with a large dataset containing millions of records. You want to perform exploratory data analysis (EDA) efficiently using NVIDIA RAPIDS on a GPU-accelerated system.
Which of the following approaches is the most efficient way to handle large-scale EDA using RAPIDS?

A. Perform all EDA using NumPy and SciPy for optimized array computations.
B. Convert the dataset into a cuDF DataFrame and perform operations like .describe() and
.value_counts() on the GPU.
C. Use Pandas directly for data manipulation and visualization.
D. Load the dataset into an Apache Spark DataFrame and run .show() to inspect the data.

Answer: B

NEW QUESTION # 198
A machine learning engineer is tasked with deploying a real-time image classification model as part of an MLOps pipeline. The model requires low-latency inference and must handle high-throughput requests efficiently.
Which of the following deployment strategies is the most suitable for leveraging GPU acceleration?

A. Using an Apache Spark cluster with distributed CPU-based inference.
B. Deploying the model using NVIDIA Triton Inference Server with TensorRT optimizations.
C. Using a REST API wrapper to load the model dynamically into memory for each request.
D. Running the model inference on a multi-core CPU server with batch processing enabled.

Answer: B

NEW QUESTION # 199
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