Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Professional
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
| GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| Machine Learning | 15% | - Model Development and Optimization
|
| Data Preparation | 17% | - Data Cleaning and Transformation
|
| MLOps | 19% | - Deployment and Monitoring
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
A financial institution is training a fraud detection model on a GPU-powered NVIDIA RAPIDS cuML pipeline. Their dataset includes customer ages, transaction amounts, merchant names, and transaction timestamps.
To optimize GPU memory usage while preserving accuracy, how should they store these features?
- A. Convert all numeric data to float64 for maximum precision, as rounding errors in lower precision could impact the model's accuracy.
- B. Keep customer ages in float16 format to reduce memory consumption, as integer types are less efficient in GPU operations.
- C. Convert timestamps into UNIX epoch integers instead of using datetime64 for more efficient GPU computations.
- D. Store transaction amounts as float32, customer ages as int8, merchant names as categorical, and timestamps as datetime64.
Correct Answer: D 🗳️
You need to generate synthetic data to augment an imbalanced dataset using RAPIDS™ and cuDF.
Which of the following strategies would be most effective in producing high-quality synthetic data for the minority class?
- A. Use synthetic data generation libraries like SDV (Synthetic Data Vault) in conjunction with cuDF to create synthetic data that mimics the distribution of the minority class.
- B. Use only the majority class data to train a model and generate synthetic data using a GAN (Generative Adversarial Network) in the RAPIDS ecosystem.
- C. Generate synthetic data by duplicating entries from the minority class using cudf.DataFrame.sample().
- D. Create synthetic data by applying random transformations to the minority class, such as scaling, rotation, or flipping, using cuDF.
Correct Answer: A 🗳️
A data scientist is working with a 50 TB dataset consisting of structured logs from IoT devices. The data needs to be cleaned, transformed, and aggregated before training a machine learning model.
Which of the following frameworks would be the most efficient choice for distributed data processing?
- A. Pandas
- B. SQLite
- C. Python multiprocessing module
- D. Apache Spark with RAPIDS Accelerator
Correct Answer: D 🗳️
You are training a convolutional neural network (CNN) model with a large dataset on a single GPU.
The model is consuming too much GPU memory, and training is slow.
Which of the following techniques would help you reduce GPU memory consumption while maintaining or improving the efficiency of training? (Select two)
- A. Use a smaller batch size to reduce the memory requirements per training iteration, allowing for faster training.
- B. Implement mixed precision training to lower memory usage and speed up computation without losing accuracy.
- C. Use batch normalization to reduce memory usage by skipping some of the computations during training.
- D. Decrease the size of the model by reducing the number of layers or the number of filters per layer.
- E. Use automatic differentiation for every operation to save memory during the backpropagation phase.
Correct Answer: A,B 🗳️
A data scientist is preparing a dataset containing numerical features with varying scales and distributions. Some features range from 0 to 1, while others have values ranging from -5000 to 5000. The dataset will be used in a machine learning model that relies on gradient-based optimization.
What is the best approach to standardizing the data to ensure uniformity across features?
- A. Apply z-score normalization (standardization) to scale features to have a mean of 0 and a standard deviation of 1
- B. Keep the features as they are since machine learning models can handle varying scales naturally
- C. Normalize the dataset by dividing each feature by its maximum absolute value
- D. Use min-max scaling to transform all features into the range [0,1]
Correct Answer: A 🗳️
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