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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Manipulation and Software Literacy	19%	- Performance profiling and optimization tools - Dependency management and containerization - Data processing libraries selection and usage - GPU-accelerated ETL workflows
Topic 2: Data Analysis	14%	- Exploratory Data Analysis (EDA) - Distributed and parallel data processing - Data visualization and graph analytics - Time-series analysis and anomaly detection
Topic 3: MLOps	19%	- Model deployment and serving - Monitoring, logging and maintenance - End-to-end workflow management - Pipeline automation and orchestration
Topic 4: Machine Learning	15%	- Model training and hyperparameter tuning - Model evaluation and validation - GPU-accelerated ML frameworks and algorithms - Distributed training strategies
Topic 5: Data Preparation	17%	- Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification - Feature engineering and data type optimization - Data validation and quality assurance
Topic 6: GPU and Cloud Computing	16%	- GPU architecture and acceleration principles - Resource management and scaling strategies - Cloud GPU environments and deployment - CRISP-DM and data science methodology

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q286-Q291):

NEW QUESTION # 286
You are working on a large-scale social network analysis project using cuGraph. Your goal is to identify the most influential individuals in the network based on their connectivity and reach.
Which of the following cuGraph algorithms would be the most appropriate choice for this task?

A. Single Source Shortest Path (SSSP)
B. Connected Components
C. PageRank
D. Louvain Clustering

Answer: C

NEW QUESTION # 287
You are working with a dataset where numerical features have different scales. To ensure uniformity across features, you decide to standardize the data using NVIDIA RAPIDS cuML.
Which of the following methods correctly standardizes the data in a GPU-accelerated manner?

A. df = (df - df.mean()) / df.std()
B. df = (df - df.min()) / (df.max() - df.min())
C. df = df.apply(lambda x: (x - x.mean()) / x.std(), axis=1)
D. 1. scaler = cuml.preprocessing.StandardScaler() 2. df = scaler.fit_transform(df)

Answer: D

NEW QUESTION # 288
You are working on a machine learning dataset with millions of rows and want to optimize performance on a GPU using CuDF. One of the features in your dataset represents categorical data with a limited number of unique values.
Which of the following data types should you use to maximize performance while reducing memory usage?

A. category
B. int64
C. object (string)
D. bool

Answer: A

NEW QUESTION # 289
You are working on an MLOps project where GPU-accelerated workflows are being used for model training. You want to benchmark and optimize these workflows to ensure the best performance.
Which of the following steps should you consider to effectively benchmark and optimize GPU- accelerated workflows? (Select two)

A. Optimize data loading by using data augmentation techniques during training to reduce the time spent on I/O operations.
B. Increase the batch size and learning rate simultaneously to maximize GPU usage and reduce training time.
C. Use a dynamic batch size strategy that adjusts the batch size based on available GPU memory to maximize throughput.
D. Use profiling tools to measure the GPU utilization and memory usage during training to identify performance bottlenecks.

Answer: C,D

NEW QUESTION # 290
You are designing a reproducible benchmark to compare the performance of deep learning models across frameworks like PyTorch and TensorFlow using NVIDIA's A100 GPU.
Which step is most critical in ensuring fair benchmarking conditions?

A. Enabling XLA compiler optimizations only for TensorFlow to enhance its performance.
B. Using a different precision setting for each framework to maximize performance per framework's capabilities.
C. Measuring only forward pass latency to compare inference speed while ignoring backward pass computation.
D. Ensuring the same CUDA/cuDNN and driver versions are installed when running benchmarks across frameworks.

Answer: D

NEW QUESTION # 291
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