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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are tasked with optimizing the performance of a large-scale data science project that involves deep learning models on a cloud infrastructure. Your organization is using GPUs for model training.
Which of the following strategies would be the most effective in optimizing GPU performance for data science tasks? (Select two)

A) Overclock the GPU to achieve higher computational speeds and improve training times.
B) Utilize multi-GPU training to parallelize the workload, reducing training time.
C) Optimize GPU performance by limiting the number of threads running on each GPU.
D) Use a single cloud instance with the largest GPU available to ensure maximum performance.
E) Use larger batch sizes to make better use of GPU memory during model training.

2. You are working on a predictive maintenance model for industrial equipment. The dataset includes various sensor readings, categorical metadata, and timestamped events.
Which data type is the best choice for a feature representing the operating status of a machine, which has three possible states: "Idle", "Running", and "Error"?

A) Floating-point representation (e.g., 0.1 for Idle, 0.5 for Running, 0.9 for Error).
B) Integer (0, 1, 2) to represent each state numerically.
C) Text (storing states as raw strings like "Idle", "Running", "Error").
D) Categorical encoding (one-hot encoding or ordinal encoding).

3. A financial services company is using NVIDIA RAPIDS cuML to train a credit risk assessment model.
The dataset contains hundreds of numerical and categorical features, including loan amount, credit score, income, employment history, and previous loan defaults.
To optimize feature selection using NVIDIA technologies, which approach should they take?

A) Use cuML's feature selection algorithms, such as Recursive Feature Elimination (RFE), to identify the most important predictors.
B) Keep all features in the dataset to ensure the model captures as much information as possible, even if some features are redundant.
C) Convert all numerical features into categorical variables to simplify the data and reduce the need for feature selection.
D) Avoid feature selection, as modern deep learning models are capable of automatically handling redundant and irrelevant features.

4. You are a data scientist working with a large dataset containing millions of records. You want to perform exploratory data analysis (EDA) efficiently using NVIDIA RAPIDS on a GPU-accelerated system.
Which of the following approaches is the most efficient way to handle large-scale EDA using RAPIDS?

A) Use Pandas directly for data manipulation and visualization.
B) Perform all EDA using NumPy and SciPy for optimized array computations.
C) Load the dataset into an Apache Spark DataFrame and run .show() to inspect the data.
D) Convert the dataset into a cuDF DataFrame and perform operations like .describe() and
.value_counts() on the GPU.

5. You are working on a large-scale machine learning project that requires preprocessing terabytes of structured and semi-structured data. You need a distributed data processing framework that can leverage NVIDIA GPUs efficiently to accelerate computations.
Which of the following approaches would best achieve this goal?

A) Using plain NumPy with CUDA extensions to manually parallelize computations across multiple GPUs
B) Using Apache Spark with PySpark for CPU-based distributed data processing
C) Using TensorFlow's Dataset API to load and preprocess the data on GPUs
D) Using Dask with RAPIDS cuDF and cuML for distributed GPU-accelerated processing

Solutions: