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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Machine Learning	15%	- 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 - Feature engineering and hyperparameter tuning 1. Batching and memory-efficient training methods 2. Feature engineering for ML models 3. Hyperparameter tuning techniques - Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch
Topic 2: GPU and Cloud Computing	16%	- Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - Performance optimization 1. Memory profiling with DLProf 2. Mixed precision and bottleneck analysis 3. Single and multi-GPU performance optimization - 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
Topic 3: Data Preparation	17%	- Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet - Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables
Topic 4: Data Manipulation and Software Literacy	19%	- 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 - Distributed computing with Dask 1. Dask-cuDF for parallel data processing 2. Scaling data operations across multiple GPUs - Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency
Topic 5: Data Analysis	14%	- Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis - Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals
Topic 6: 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. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management - Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies

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NCP-ADS Certification Test Answers - NCP-ADS Exam Syllabus

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q87-Q92):

NEW QUESTION # 87
You are processing a large dataset using NVIDIA Dask-cuDF to distribute GPU-accelerated computation across multiple nodes. Users report inconsistent execution times, with some jobs taking significantly longer than expected.
Which of the following actions would best help diagnose the performance bottleneck?

A. Switch to using Pandas with Dask to compare execution speed differences.
B. Reduce the number of Dask workers to minimize parallel execution overhead.
C. Limit GPU memory usage to force more frequent spilling to disk and observe performance differences.
D. Use Dask's dashboard and NVTX markers to analyze task execution times and GPU utilization.

Answer: D

NEW QUESTION # 88
A data scientist is working with a large dataset containing missing values and outliers. The dataset will be used for training a machine learning model. The scientist decides to preprocess the data using RAPIDS cuDF, an accelerated dataframe library.
Which of the following is the most efficient approach to handle missing values while maintaining data integrity?

A. Replace missing values with zero using df.fillna(0).
B. Use df.dropna() to remove all rows with missing values.
C. Convert missing values to a separate categorical class using df.fillna("missing").
D. Use df.fillna(df.mean()) to replace missing values with the column mean.

Answer: D

NEW QUESTION # 89
You are comparing the performance of GPU-accelerated deep learning models on two cloud platforms: AWS EC2 and Google Cloud Platform (GCP). You want to design a benchmark that evaluates GPU resource utilization, processing time, and cost-efficiency for training models with large datasets.
Which actions should you take to implement an effective benchmark? (Select two)

A. Only consider the cost of GPU resources for each cloud provider without factoring in data storage and transfer costs.
B. Measure both GPU utilization and network latency between the cloud platform and your data storage location.
C. Run each model on the same GPU type and instance size across both cloud platforms.
D. Limit the benchmarking tests to a single model architecture for each cloud platform.

Answer: B,C

NEW QUESTION # 90
You have trained a machine learning model and are deploying it into production in a cloud-based environment. You need to ensure the model is continuously monitored for performance, including drift in input data and prediction quality.
Which of the following techniques should you use to effectively monitor the model in production? (Select two)

A. Set up continuous model retraining pipelines triggered by data drift or performance degradation.
B. Implement model versioning and rollback procedures to maintain consistency in production models.
C. Use manual inspection of model predictions periodically to assess accuracy in production.
D. Restrict access to the model's API to minimize the number of calls and reduce monitoring overhead.

Answer: A,B

NEW QUESTION # 91
You are training a convolutional neural network (CNN) model with a large dataset on a single GPU.
The model is consuming too much GPU memory, and training is slow.
Which of the following techniques would help you reduce GPU memory consumption while maintaining or improving the efficiency of training? (Select two)

A. Implement mixed precision training to lower memory usage and speed up computation without losing accuracy.
B. Decrease the size of the model by reducing the number of layers or the number of filters per layer.
C. Use a smaller batch size to reduce the memory requirements per training iteration, allowing for faster training.
D. Use automatic differentiation for every operation to save memory during the backpropagation phase.
E. Use batch normalization to reduce memory usage by skipping some of the computations during training.

Answer: A,C

NEW QUESTION # 92
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