NCP-ADS Sample Questions & New NCP-ADS Exam Test

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q211-Q216):

NEW QUESTION # 211
You are working with a time-series dataset containing network traffic logs. You suspect the presence of anomalies, such as distributed denial-of-service (DDoS) attacks or sudden traffic surges.
Which machine learning approach is best suited for detecting these anomalies?

A. Autoencoder-based Anomaly Detection
B. Linear Regression
C. k-Nearest Neighbors (k-NN) for Classification
D. Naive Bayes Classifier

Answer: A

NEW QUESTION # 212
A data scientist is analyzing a large time-series dataset containing stock price movements of thousands of companies over a decade. The dataset is stored as a cuDF DataFrame and contains millions of rows. The scientist wants to visualize trends and patterns interactively while leveraging GPU acceleration.
Which of the following approaches is the most efficient for visualizing this time-series data?

A. Use cuXfilter with a cuDF DataFrame to generate interactive visualizations directly on the GPU.
B. Use matplotlib with plt.plot() while applying df.to_pandas() to convert data.
C. Convert the cuDF DataFrame to Pandas and use matplotlib for plotting.
D. Use seaborn with a sampled subset of the dataset to generate line plots.

Answer: A

NEW QUESTION # 213
A data scientist is analyzing sales data for an e-commerce company that experiences strong seasonal trends (e.g., increased sales during holiday seasons). The goal is to accurately forecast future sales using GPU-accelerated data science techniques.
Which approach would be the most effective?

A. Train a Decision Tree model using cuML to classify future sales trends.
B. Compute a rolling average and manually adjust for seasonality based on previous peak sales months.
C. Apply k-Means clustering to identify seasonal patterns and extrapolate future values.
D. Use a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and optimize it using NVIDIA RAPIDS.

Answer: D

NEW QUESTION # 214
You are working with a large dataset containing 500 million records stored as a parquet file. Your task is to perform data filtering, aggregation, and transformation as efficiently as possible.
Which of the following approaches would be the best choice for accelerated data manipulation using NVIDIA technologies?

A. Convert the dataset to a NumPy array and use standard Python loops
B. Load the dataset into pandas and use apply() for transformations
C. Use multiprocessing in Python to parallelize pandas operations across CPU cores
D. Use RAPIDS cuDF to load, filter, and transform the dataset on the GPU

Answer: D

NEW QUESTION # 215
You are setting up a deep learning model for training on a multi-GPU cluster. You want to maximize training efficiency while maintaining model convergence.
Which of the following strategies is most effective in ensuring efficient multi-GPU training?

A. Train each GPU independently on a different dataset to reduce the communication bottleneck.
B. Use Model Parallelism, where different layers of the model are placed on different GPUs to reduce communication overhead.
C. Use Data Parallelism, where each GPU gets a different portion of the dataset, but gradients are averaged across all GPUs.
D. Reduce batch size to ensure each GPU receives only a small portion of the training data.

Answer: C

NEW QUESTION # 216
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