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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
GPU and Cloud Computing	16%	- GPU resource management 1. Efficient GPU resource allocation and scheduling - 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 - Performance optimization 1. Single and multi-GPU performance optimization 2. Mixed precision and bottleneck analysis 3. Memory profiling with DLProf
Data Manipulation and Software Literacy	19%	- 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 - Distributed computing with Dask 1. Scaling data operations across multiple GPUs 2. Dask-cuDF for parallel data processing
Data Analysis	14%	- 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 - 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
Machine Learning	15%	- Feature engineering and hyperparameter tuning 1. Batching and memory-efficient training methods 2. Hyperparameter tuning techniques 3. Feature engineering for ML models - Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch - 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 Preparation	17%	- Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling - 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 - Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance
MLOps	19%	- Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools - Model deployment and serving 1. Production deployment strategies 2. Model saving, loading, and prediction generation - Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q27-Q32):

NEW QUESTION # 27
You are working on a large dataset for a machine learning model that will be trained using RAPIDS cuML. The dataset includes categorical, integer, and floating-point features.
Which of the following approaches is the best practice for determining the optimal data type choice for each feature using NVIDIA's RAPIDS cuDF library?

A. Use float16 for all floating-point data to reduce memory usage and increase GPU processing speed.
B. Use float32 instead of float64 for floating-point numbers when possible, and leverage int8, int16, or int32 for categorical and integer data based on their range.
C. Convert all numerical data to float64 for maximum precision in calculations.
D. Convert categorical variables into int8 to optimize GPU memory usage.

Answer: B

NEW QUESTION # 28
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 log transformation to all numerical columns to force them into a uniform distribution.
B. Apply z-score normalization (standardization) to scale values based on mean and standard deviation.
C. Convert all numerical features to categorical values using binning.
D. Use Min-Max scaling to transform values into a fixed range (e.g., [0,1] or [-1,1]).

Answer: B

NEW QUESTION # 29
You are working on optimizing a deep learning model for inference on an NVIDIA GPU. You decide to use NVIDIA DLProf to profile the model and analyze its performance. After running DLProf, you review the generated reports and find that the GPU Utilization is significantly lower than expected.
Which of the following is the most likely reason for this issue, as indicated by the profiling data?

A. DLProf detected a high level of tensor core utilization, which generally indicates poor performance.
B. The GPU lacks sufficient VRAM, causing frequent memory swaps to system RAM.
C. The batch size is too large, leading to excessive memory allocation failures.
D. The model contains a large number of small, inefficient kernel launches that introduce overhead.

Answer: D

NEW QUESTION # 30
A financial institution is developing an ETL pipeline to ingest and process large volumes of streaming data from various sources, including stock market feeds, real-time transactions, and economic indicators. The ETL process must be highly efficient to minimize latency while ensuring data integrity.
Which of the following strategies is best suited for implementing a high-performance, GPU-accelerated ETL pipeline?

A. Utilize NVIDIA Morpheus with RAPIDS to preprocess real-time streaming data using GPU acceleration.
B. Use Pandas and Python's built-in threading library to handle concurrent data ingestion and transformation.
C. Store all streaming data in a PostgreSQL database before performing batch transformations.
D. Load data directly into an Excel spreadsheet and use VBA macros to clean and transform it.

Answer: A

NEW QUESTION # 31
You are working on a dataset containing missing values, duplicate records, and inconsistent data types.
The dataset size is 15GB and you need to efficiently perform data cleansing operations such as:
- Handling missing values
- Dropping duplicates
- Converting data types
Which of the following approaches would be the most efficient way to perform these operations on an NVIDIA GPU?

A. Use Vaex to perform data cleansing, as it is optimized for large-scale datasets
B. Use pandas to load the dataset and perform the operations using standard pandas functions
C. Convert the dataset into a NumPy array and process it using CuPy before converting it back to a DataFrame
D. Load the dataset using cuDF, then use cuDF's built-in .dropna(), .drop_duplicates(), and .astype() methods

Answer: D

NEW QUESTION # 32
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