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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling - Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance - Data loading and preprocessing 1. NVIDIA DALI for high-performance data loading 2. Handling class imbalance and generating synthetic data - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines
Topic 2: 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 - Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation - Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools
Topic 3: Data Analysis	14%	- Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Visualization 1. Selecting appropriate plots for different analysis goals 2. Visualizing data using Plotly and Matplotlib - Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets
Topic 4: GPU and Cloud Computing	16%	- Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - GPU architecture and fundamentals 1. GPU architecture fundamentals for data science 2. CPU vs GPU workloads and memory transfer optimization - GPU resource management 1. Efficient GPU resource allocation and scheduling - Performance optimization 1. Mixed precision and bottleneck analysis 2. Memory profiling with DLProf 3. Single and multi-GPU performance optimization
Topic 5: 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. Groupby, apply, and aggregation operations 2. Data integration, joining, merging, and filtering 3. cuDF vs pandas API mapping and usage
Topic 6: Machine Learning	15%	- 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. Selection of appropriate algorithms for GPU execution 3. Training models using cuML and GPU-accelerated XGBoost - Feature engineering and hyperparameter tuning 1. Hyperparameter tuning techniques 2. Feature engineering for ML models 3. Batching and memory-efficient training methods

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NVIDIA NCP-ADS Quiz - NCP-ADS Studienanleitung & NCP-ADS Trainingsmaterialien

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NVIDIA-Certified-Professional Accelerated Data Science NCP-ADS Prüfungsfragen mit Lösungen (Q76-Q81):

76. Frage
You are working with structured tabular data in a cloud-based GPU environment.
Your dataset contains the following columns:
Column Name Example Values Data Type Needed
user_id 15432, 98765, 43210 Integer
purchase_amt 12.99, 35.50, 100.75 Floating Point
category 'Books', 'Electronics' Categorical
Which of the following is the most optimal approach to assign data types to these columns to ensure efficient memory usage and computational performance?

A. 1. df['user_id'] = df['user_id'].astype('int16')
2. df['purchase_amt'] = df['purchase_amt'].astype('float16')
3. df['category'] = df['category'].astype('string')
B. 1. df['user_id'] = df['user_id'].astype('int64')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')
C. 1. df['user_id'] = df['user_id'].astype('float32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')
D. 1. df['user_id'] = df['user_id'].astype('int32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float32')
3. df['category'] = df['category'].astype('category')

Antwort: D

77. Frage
You are designing an ETL workflow to process large-scale financial transaction data using GPU acceleration. The dataset is stored in a Parquet file and contains millions of records.
Which of the following approaches is the most efficient for performing extract, transform, and load (ETL) operations using NVIDIA RAPIDS technologies?

A. Load the Parquet file directly into a cuDF DataFrame and use cuDF's built-in functions for transformations before writing the results back to storage.
B. Use Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics.
C. Store all data as CSV files and perform ETL operations using traditional row-based processing.
D. Use Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.

Antwort: A

78. Frage
A machine learning engineer is working on a dataset with thousands of numerical features. The dataset is too large for standard CPU-based processing, so the engineer decides to leverage GPUs for efficient feature engineering.
Which of the following techniques is the most suitable for dimensionality reduction using GPU acceleration?

A. Applying RAPIDS cuML's PCA (Principal Component Analysis) to reduce feature dimensions efficiently.
B. Encoding all numerical features as categorical variables to reduce dimensionality.
C. Using manual feature selection by dropping columns with low variance on a CPU.
D. Converting the dataset into a sparse matrix and running traditional singular value decomposition (SVD) on a CPU.

Antwort: A

79. Frage
A financial services company is deploying an AI-driven risk assessment model using NVIDIA GPUs on a cloud platform. To optimize resource utilization and cost efficiency, they need to determine the best GPU deployment strategy.
Which of the following is the most effective approach?

A. Run all AI workloads on a single large GPU instance without any partitioning or workload separation.
B. Choose an on-demand cloud instance with an outdated GPU model to reduce costs, even if performance is compromised.
C. Deploy the model using NVIDIAAI Enterprise with MIG (Multi-Instance GPU) to allocate multiple workloads on a single GPU efficiently.
D. Deploy separate full-GPU instances for each workload, even if they have variable compute demands.

Antwort: C

80. Frage
You are tasked with implementing data caching to reduce shuffle in an accelerated machine learning pipeline using NVIDIA technologies. You need to cache intermediate results after a shuffle operation in a distributed setting.
Which of the following is the best approach to minimize shuffle overhead and maximize performance?

A. Cache the data in GPU memory using RAPIDS cuDF for faster access, and leverage GPU-based partitioning to reduce shuffle size.
B. Implement Spark's default disk caching to store shuffle results, allowing the GPU to access disk data directly.
C. Use RAPIDS cuDF to cache the shuffled data on disk and then re-load it from disk during subsequent stages.
D. Use DALI to perform preprocessing and cache the output before the shuffle operation, thereby eliminating the need for shuffle.

Antwort: C

81. Frage
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