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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
MLOps	19%	- 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 - 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. Production deployment strategies 2. Model saving, loading, and prediction generation
Data Preparation	17%	- Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues - 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 - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables
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 - 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)
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. Batching and memory-efficient training methods 2. Feature engineering for ML models 3. Hyperparameter tuning techniques - Model training with GPU acceleration 1. Multi-GPU training strategies 2. Training models using cuML and GPU-accelerated XGBoost 3. Selection of appropriate algorithms for GPU execution
Data Analysis	14%	- Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals
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 - 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 - Performance optimization 1. Single and multi-GPU performance optimization 2. Memory profiling with DLProf 3. Mixed precision and bottleneck analysis

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q286-Q291):

NEW QUESTION # 286
A data scientist is working with a large dataset containing missing values and outliers. The dataset will be used for training a machine learning model. The scientist decides to preprocess the data using RAPIDS cuDF, an accelerated dataframe library.
Which of the following is the most efficient approach to handle missing values while maintaining data integrity?

A. Convert missing values to a separate categorical class using df.fillna("missing").
B. Replace missing values with zero using df.fillna(0).
C. Use df.fillna(df.mean()) to replace missing values with the column mean.
D. Use df.dropna() to remove all rows with missing values.

Answer: C

NEW QUESTION # 287
A data science team wants to deploy a GPU-accelerated pipeline using cuGraph to analyze graph data on cloud infrastructure. They are evaluating different cloud-based GPU solutions.
Which of the following factors should they consider when selecting a cloud-based GPU instance for running cuGraph efficiently?

A. The choice of GPU instance does not affect cuGraph performance since all GPUs execute graph algorithms at the same speed.
B. Cloud-based GPUs are only useful for rendering graphics, not for running cuGraph algorithms.
C. The availability of NVIDIA CUDA-enabled GPUs, as cuGraph requires CUDA for acceleration.
D. cuGraph runs equally well on CPU-based virtual machines, making GPU instances unnecessary.

Answer: C

NEW QUESTION # 288
You are working on a large dataset for a machine learning model and need to preprocess the data efficiently using NVIDIA RAPIDS cuDF on a GPU-accelerated system.
Which of the following statements is correct regarding data preparation using cuDF?

A. cuDF operations are executed lazily and require an explicit compute() call to execute transformations.
B. cuDF supports common Pandas-like operations, such as filtering, merging, and groupby, but performs them efficiently on the GPU.
C. cuDF can be used as a direct replacement for Pandas, but it is optimized for multi-node distributed computing rather than GPU acceleration.
D. cuDF requires all input data to be stored in Apache Parquet format, as it does not support CSV or JSON ingestion.

Answer: B

NEW QUESTION # 289
You have a structured dataset containing 20 million records with missing values in several columns.
You need to fill missing values while ensuring that the approach is optimal for execution on NVIDIA GPUs.
Which method should you use?

A. Use cuDF's .fillna() method to replace missing values in GPU memory
B. Load the dataset into Modin with a Dask backend and use .fillna() for parallel execution
C. Convert the dataset to a pandas DataFrame, fill missing values, and then convert it back to cuDF
D. Drop all missing values using .dropna() instead of filling them, as GPU memory is limited

Answer: A

NEW QUESTION # 290
In Python, when working with large datasets using pandas, which of the following methods are best for improving performance and efficiency when applying operations on DataFrames? (Select two)

A. Using for loops to apply operations row by row
B. Using vectorized operations (e.g., element-wise arithmetic)
C. Using map() function to apply a function element-wise
D. Using apply() function over DataFrame rows
E. Using iterrows() for iterating through DataFrame rows

Answer: B,C

NEW QUESTION # 291
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