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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which of the following methods are commonly used to handle missing data in data analysis? (Select two)

A) Ignoring missing data entirely
B) Removing rows with missing data
C) Imputing with the mean or median of the feature
D) Using a fixed constant value for imputation
E) Using the mode to impute missing values

2. Which of the following actions can you perform using DLProf to analyze a deep learning model's performance?

A) Modify the training dataset during model execution
B) Visualize GPU memory utilization over time
C) Automatically adjust the learning rate based on the model's convergence
D) Increase batch size to improve accuracy

3. 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 only the majority class data to train a model and generate synthetic data using a GAN (Generative Adversarial Network) in the RAPIDS ecosystem.
B) Generate synthetic data by duplicating entries from the minority class using cudf.DataFrame.sample().
C) 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.
D) Create synthetic data by applying random transformations to the minority class, such as scaling, rotation, or flipping, using cuDF.

4. You are working with a dataset containing 2 billion rows of financial transactions, and you need to perform exploratory data analysis (EDA) before building a predictive model.
Which of the following approaches is the most appropriate for handling this data efficiently?

A) Load the entire dataset into a Pandas DataFrame and analyze it using Pandas built-in methods.
B) Use an accelerated data science framework such as RAPIDS cuDF or Dask to distribute computations across GPUs.
C) Use SQLite to store the data locally and run queries sequentially to minimize memory consumption.
D) Convert the dataset to JSON format and use Python's built-in json module to parse and analyze it efficiently.

5. When utilizing GPU instances in a cloud environment for data science, which of the following are common considerations? (Select two)

A) GPUs are suitable for both training and inference tasks but not for preprocessing
B) Cost-effective GPU instances always provide the best performance
C) Cloud providers offer dedicated GPU instances for high-performance needs
D) GPU performance can be degraded by network latency in distributed training

Solutions: