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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- 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 - 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
Topic 2: MLOps	19%	- Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management - 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. Model saving, loading, and prediction generation 2. Production deployment strategies
Topic 3: Data Analysis	14%	- 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 - Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph - 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. 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 - Distributed computing with Dask 1. Dask-cuDF for parallel data processing 2. Scaling data operations across multiple GPUs
Topic 5: GPU and Cloud Computing	16%	- GPU resource management 1. Efficient GPU resource allocation and scheduling - 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 - Performance optimization 1. Single and multi-GPU performance optimization 2. Memory profiling with DLProf 3. Mixed precision and bottleneck analysis
Topic 6: 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. Feature engineering for ML models 2. Batching and memory-efficient training methods 3. Hyperparameter tuning techniques - Model training with GPU acceleration 1. Training models using cuML and GPU-accelerated XGBoost 2. Multi-GPU training strategies 3. Selection of appropriate algorithms for GPU execution

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q270-Q275):

NEW QUESTION # 270
You are tasked with implementing a multi-GPU data pipeline using Dask-CUDA to process large datasets stored in Parquet format. Your goal is to achieve optimal GPU memory utilization and minimize inter-GPU communication overhead.
Which of the following approaches best aligns with these goals?

A. Use dask.persist() instead of dask.compute() to force immediate execution of tasks before distribution to GPUs.
B. Use dask_cudf.read_parquet() with split_row_groups=True to evenly distribute data across GPUs.
C. Use dask.array instead of dask_cudf because it provides better performance for structured tabular data.
D. Set dask.config.set({'distributed.worker.memory.target': 0.9}) to allocate 90% of the total CPU memory for GPU operations.

Answer: B

NEW QUESTION # 271
In the context of the CRISP-DM process, you are tasked with building a machine learning model on a large dataset. You decide to leverage GPU resources to accelerate training.
Which of the following steps in the CRISP-DM process are most likely to benefit from the use of GPU resources in the cloud environment? (Select two)

A. Data Exploration
B. Model Training and Evaluation
C. Business Understanding
D. Data Understanding
E. Model Deployment and Monitoring

Answer: A,B

NEW QUESTION # 272
You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?

A. Use a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values.
B. Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration.
C. Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.
D. Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.

Answer: B

NEW QUESTION # 273
You are working with a large time-series dataset consisting of millions of records and want to efficiently visualize trends over time using NVIDIA technologies. The dataset is stored as a cuDF DataFrame, and you need to generate an interactive line plot with minimal performance overhead.
Which of the following is the best approach to achieve this goal?

A. Use the hvPlot library with RAPIDS cuDF to directly render the time-series data interactively
B. Convert the cuDF DataFrame to a Pandas DataFrame and plot using Matplotlib
C. Load the data into a Spark DataFrame and visualize using Apache Zeppelin
D. Use the Bokeh library to plot the time-series data from a cuDF DataFrame directly

Answer: A

NEW QUESTION # 274
You are designing an accelerated ETL pipeline to process large-scale datasets in a data science workflow.
Which of the following are key considerations when selecting the right tools and methods for implementing this pipeline? (Select two)

A. Using a single storage location for both raw and transformed data to simplify the workflow.
B. Relying on traditional single-threaded processing for the extraction phase to reduce complexity.
C. Leveraging parallel processing and distributed computing frameworks like Apache Spark to speed up the transformation phase.
D. Using GPU-accelerated libraries such as RAPIDS for data transformation to enhance processing speed.
E. Ensuring the ETL pipeline uses only batch processing for data ingestion.

Answer: C,D

NEW QUESTION # 275
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