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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q258-Q263):

NEW QUESTION # 258
You are working with a large dataset in a cloud environment for a deep learning model. The dataset consists of several features including numerical values, categorical data, and timestamps.
Which of the following choices would result in the most efficient use of GPU and cloud resources when determining the optimal data type for each feature? (Select three)

A. Use int8 for categorical features where there are fewer than 256 categories.
B. Use datetime64[ns] for timestamp features to ensure high precision.
C. Use int32 for all numerical features to save memory.
D. Use float64 for all numerical features to ensure maximum precision.
E. Use object data types for categorical features to avoid type conversion overhead.

Answer: A,B,C

NEW QUESTION # 259
A financial institution is training a fraud detection model on a GPU-powered NVIDIA RAPIDS cuML pipeline. Their dataset includes customer ages, transaction amounts, merchant names, and transaction timestamps.
To optimize GPU memory usage while preserving accuracy, how should they store these features?

A. Convert all numeric data to float64 for maximum precision, as rounding errors in lower precision could impact the model's accuracy.
B. Convert timestamps into UNIX epoch integers instead of using datetime64 for more efficient GPU computations.
C. Store transaction amounts as float32, customer ages as int8, merchant names as categorical, and timestamps as datetime64.
D. Keep customer ages in float16 format to reduce memory consumption, as integer types are less efficient in GPU operations.

Answer: C

NEW QUESTION # 260
Which of the following tools in the NVIDIA AI stack is specifically designed to accelerate the deployment of machine learning models for production by optimizing inference performance?

A. DLA (Deep Learning Accelerator)
B. cuBLAS
C. cuML
D. Triton Inference Server

Answer: D

NEW QUESTION # 261
A data scientist is working with a dataset of sensor readings (temperature, pressure, vibration) in different scales and units. To ensure all features contribute equally to a machine learning model, the data needs to be standardized.
Which approach is best for standardizing numerical features?

A. Apply z-score normalization (standardization) to scale values based on mean and standard deviation.
B. Convert all numerical features to categorical values using binning.
C. Apply log transformation to all numerical columns to force them into a uniform distribution.
D. Use Min-Max scaling to transform values into a fixed range (e.g., [0,1] or [-1,1]).

Answer: A

NEW QUESTION # 262
Which of the following is the main advantage of using TensorRT for inference in an accelerated data science pipeline?

A. TensorRT optimizes deep learning models to run efficiently on NVIDIA GPUs by reducing precision while maintaining accuracy.
B. TensorRT is only compatible with image classification models and does not support other model types.
C. TensorRT automatically builds training models from raw data without requiring pre-trained models.
D. TensorRT is mainly used for data visualization and not for model inference.

Answer: A

NEW QUESTION # 263
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