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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Manipulation and Software Literacy | 19% | - GPU-accelerated data manipulation using cuDF
|
| Data Analysis | 14% | - Exploratory data analysis
|
| MLOps | 19% | - Model monitoring and management
|
| Data Preparation | 17% | - Data cleaning and quality handling
|
| Machine Learning | 15% | - Feature engineering and hyperparameter tuning
|
| GPU and Cloud Computing | 16% | - Cloud GPU environments
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
A machine learning engineer is training a large transformer-based model for natural language processing (NLP). They want to maximize training speed and efficiency using NVIDIA GPUs.
Which of the following techniques would most effectively enhance GPU utilization and reduce training time?
- A. Disabling data parallelism
- B. Prefetching data with the CPU while training on the GPU
- C. Running training exclusively on CPU
- D. Using mixed-precision training with Tensor Cores
Correct Answer: D 🗳️
Which of the following best describes the purpose of the NVIDIA TensorRT library?
- A. Provides hardware abstraction for AI model development
- B. Optimizes and accelerates inference of trained models
- C. Accelerates training of neural networks
- D. Manages GPU resources for deep learning models
Correct Answer: B 🗳️
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. Optimize the data pipeline using cudf.DataFrame.merge() to improve data loading speed.
- B. Compute model accuracy, precision, and recall using cuml.metrics.accuracy_score() and cuml.metrics.classification_report().
- C. Deploy the model to an edge device using TensorRT for real-time inference.
- D. Define the problem statement and collect relevant datasets before training the model.
Correct Answer: B 🗳️
You are performing data cleansing on a large dataset using CuDF. The dataset contains numerical values, some of which are outliers. You need to remove or adjust these outliers to make your model training more robust.
Which of the following approaches should you consider for handling outliers efficiently in CuDF? (Select two)
- A. Using dropna() to remove rows with outliers
- B. Using quantile() to calculate the interquartile range (IQR) and filter out outliers
- C. Using applymap() to apply a custom function for handling outliers
- D. Using clip() to set a maximum and minimum threshold for numerical values
Correct Answer: B,D 🗳️
You are tasked with acquiring a dataset for training a machine learning model in healthcare, predicting patient readmission rates. Before using the dataset, you must assess its quality.
Which of the following is the most important factor to evaluate before acquisition?
- A. The number of missing values and inconsistencies in key columns
- B. Whether the dataset is stored on a cloud server or a local machine
- C. The programming language used to preprocess the dataset
- D. The file format (CSV, JSON, or XML) of the dataset
Correct Answer: A 🗳️
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