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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
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. cuDF vs pandas API mapping and usage 2. Data integration, joining, merging, and filtering 3. Groupby, apply, and aggregation operations
GPU and Cloud Computing	16%	- Performance optimization 1. Mixed precision and bottleneck analysis 2. Memory profiling with DLProf 3. Single and multi-GPU performance 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 - GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science
Data Analysis	14%	- 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 - Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets
Data Preparation	17%	- Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance
MLOps	19%	- Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools - 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. Conda environment management 2. Docker for reproducible GPU-accelerated workflows - Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies
Machine Learning	15%	- 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. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts - 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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q261-Q266):

NEW QUESTION # 261
You have trained a machine learning model using cuML as part of the Modeling phase in the CRISP- DM framework. Now, you need to assess how well the model performs before moving forward with deployment.
Which of the following steps aligns best with the Evaluation phase of CRISP-DM using NVIDIA technologies?

A. Deploy the model to an edge device using TensorRT for real-time inference.
B. Compute model accuracy, precision, and recall using cuml.metrics.accuracy_score() and cuml.metrics.classification_report().
C. Define the problem statement and collect relevant datasets before training the model.
D. Optimize the data pipeline using cudf.DataFrame.merge() to improve data loading speed.

Answer: B

NEW QUESTION # 262
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. Use Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics.
B. Store all data as CSV files and perform ETL operations using traditional row-based processing.
C. Use Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.
D. 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.

Answer: D

NEW QUESTION # 263
Which of the following is the most appropriate way to perform large-scale data processing in a GPU- accelerated environment using NVIDIA RAPIDS?

A. Use pandas for all data manipulations and rely on multi-threading for parallel execution.
B. Use TensorFlow for all data manipulations in a GPU environment.
C. Use Dask on top of RAPIDS for distributed computing across multiple GPUs.
D. Use NumPy exclusively for processing large datasets on GPUs.

Answer: C

NEW QUESTION # 264
A data scientist is working on a dataset where the numerical features have different ranges, and they need to ensure uniformity across features before training a machine learning model.
Which of the following approaches, utilizing NVIDIA technologies, would best achieve this goal?

A. Use cuML's StandardScaler() to transform the features to have zero mean and unit variance.
B. Apply cuML's RobustScaler() to center the data using median and scale using the interquartile range.
C. Use cuML's PCA to directly remove the need for standardization by reducing dimensionality.
D. Apply cuDF's normalize() function to scale each feature between 0 and 1.

Answer: A

NEW QUESTION # 265
A company is deploying an MLOps pipeline for training and serving deep learning models. The data scientists want to leverage GPU acceleration at multiple stages of the pipeline to enhance efficiency.
Which of the following steps would benefit the most from GPU acceleration?

A. Training and inference workloads using deep learning models with TensorFlow or PyTorch.
B. Storing and retrieving models from a centralized object storage system.
C. Model monitoring by logging metadata and performance metrics in a database.
D. Running CI/CD workflows for code integration and deployment using a traditional CPU-based Jenkins setup.

Answer: A

NEW QUESTION # 266
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