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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 - GPU resource management 1. Efficient GPU resource allocation and scheduling - Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - Performance optimization 1. Single and multi-GPU performance optimization 2. Mixed precision and bottleneck analysis 3. Memory profiling with DLProf
Topic 2: MLOps	19%	- Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - Experiment tracking 1. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware - Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows
Topic 3: 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 - 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
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. Groupby, apply, and aggregation operations 3. cuDF vs pandas API mapping and usage - Distributed computing with Dask 1. Scaling data operations across multiple GPUs 2. Dask-cuDF for parallel data processing
Topic 5: Data Analysis	14%	- Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - Visualization 1. Selecting appropriate plots for different analysis goals 2. Visualizing data using Plotly and Matplotlib - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis
Topic 6: 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. Feature engineering for ML models 2. Hyperparameter tuning techniques 3. Batching and memory-efficient training methods

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q126-Q131):

NEW QUESTION # 126
You need to deploy a machine learning model on a GPU-equipped system. The GPU has 16GB of VRAM, and the model requires approximately 12GB of memory during inference. However, additional system processes and other applications consume 5GB of VRAM.
What would happen if you attempt to run inference without making any optimizations, and how should you resolve the issue?

A. The model will run successfully but with reduced performance due to memory fragmentation
B. The model will run without issues because 16GB of VRAM is sufficient for a 12GB model
C. Switching from a GPU to CPU inference will resolve memory issues without performance loss
D. The model will fail to run due to out-of-memory (OOM) errors, and using a smaller batch size can help reduce memory usage

Answer: D

NEW QUESTION # 127
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 TensorFlow for all data manipulations in a GPU environment.
B. Use NumPy exclusively for processing large datasets on GPUs.
C. Use Dask on top of RAPIDS for distributed computing across multiple GPUs.
D. Use pandas for all data manipulations and rely on multi-threading for parallel execution.

Answer: C

NEW QUESTION # 128
You are working on a data science project that involves processing large-scale financial transaction data. You want to optimize data manipulation operations using NVIDIA's RAPIDS cuDF.
Which of the following approaches best leverages NVIDIA technologies for efficient data manipulation?

A. Preprocess the data using Apache Spark's CPU-based DataFrame API before transferring it to a GPU for machine learning.
B. Load the dataset into a Pandas DataFrame and use multi-threading to speed up operations.
C. Convert the dataset into a SQLite database and execute SQL queries to perform data transformations.
D. Use cuDF DataFrames for data manipulation and rely on GPU-accelerated functions like .groupby(),
.merge(), and .applymap().

Answer: D

NEW QUESTION # 129
You are working on an MLOps pipeline that involves loading a large dataset for training a deep learning model on an NVIDIA GPU. Before training, you need to ensure that the dataset fits within the available GPU memory.
Which of the following commands in Python using the pandas and numpy libraries can correctly determine the memory size of a dataset?

A. sys.getsizeof(df)
B. df.info(memory_usage='deep')
C. df.memory_usage(deep=True).sum()
D. np.array(df).nbytes

Answer: C

NEW QUESTION # 130
A data engineering team is tasked with processing terabytes of log data every hour using an ETL pipeline. Due to the large data volume, they need a scalable GPU-accelerated solution that can distribute data processing across multiple GPUs.
Which approach best meets their needs?

A. Use cuDF alone for processing log data, as it provides optimal performance on a single GPU.
B. Process data using Pandas, then export the results to a CSV file for GPU-accelerated analytics.
C. Use NumPy for data transformations before converting the dataset into cuDF for final storage.
D. Use Dask-cuDF to distribute cuDF DataFrame operations across multiple GPUs, enabling parallel ETL processing.

Answer: D

NEW QUESTION # 131
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