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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	14%	- Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis - 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
Topic 2: Machine Learning	15%	- 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 - 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. Hyperparameter tuning techniques 3. Feature engineering for ML models
Topic 3: Data Manipulation and Software Literacy	19%	- 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) - GPU-accelerated data manipulation using cuDF 1. Data integration, joining, merging, and filtering 2. cuDF vs pandas API mapping and usage 3. Groupby, apply, and aggregation operations
Topic 4: 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 - Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management
Topic 5: 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. Memory profiling with DLProf 2. Single and multi-GPU performance optimization 3. Mixed precision and bottleneck analysis
Topic 6: Data Preparation	17%	- Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables - Data loading and preprocessing 1. NVIDIA DALI for high-performance data loading 2. Handling class imbalance and generating synthetic data - Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q214-Q219):

NEW QUESTION # 214
You are using RAPIDS cuML to train a regression model on a dataset with features of varying scales (temperature in Celsius, revenue in thousands, customer age). To improve model performance, you decide to standardize the data.
Which approach correctly standardizes the data using NVIDIA technologies?

A. Use cuml.MinMaxScaler() to scale the features to a range of [0,1] without adjusting for mean and variance.
B. Use cuml.PCA() to reduce the dimensionality of the dataset, which also standardizes feature variance.
C. Use numpy.mean() and numpy.std() to manually standardize the dataset before feeding it into the GPU.
D. Use cuml.StandardScaler() to transform the features to have a mean of zero and a standard deviation of one.

Answer: D

NEW QUESTION # 215
Which of the following techniques are commonly used to identify and acquire datasets for machine learning and data science projects? (Select three)

A. Public APIs
B. Data augmentation
C. Crowdsourcing
D. Manual data entry
E. Web scraping

Answer: A,C,E

NEW QUESTION # 216
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. Load the dataset using cuDF, then use cuDF's built-in .dropna(), .drop_duplicates(), and .astype() methods
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. Use Vaex to perform data cleansing, as it is optimized for large-scale datasets

Answer: A

NEW QUESTION # 217
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. The batch size is too large, leading to excessive memory allocation failures.
B. DLProf detected a high level of tensor core utilization, which generally indicates poor performance.
C. The model contains a large number of small, inefficient kernel launches that introduce overhead.
D. The GPU lacks sufficient VRAM, causing frequent memory swaps to system RAM.

Answer: C

NEW QUESTION # 218
A data scientist is working with a large dataset for a machine learning model and wants to accelerate feature engineering using a GPU.
Which of the following approaches will provide the most significant performance boost when using GPU acceleration?

A. Using RAPIDS cuDF and cuML libraries to perform feature transformations on a GPU.
B. Using a single-threaded feature extraction approach to avoid overhead from parallelization.
C. Reducing dataset size by randomly removing data points without considering class balance.
D. Using traditional pandas DataFrames and NumPy operations optimized for CPU processing.

Answer: A

NEW QUESTION # 219
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