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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with a dataset where numerical features have different scales. To ensure uniformity across features, you decide to standardize the data using NVIDIA RAPIDS cuML.
Which of the following methods correctly standardizes the data in a GPU-accelerated manner?

A) df = df.apply(lambda x: (x - x.mean()) / x.std(), axis=1)
B) df = (df - df.min()) / (df.max() - df.min())
C) df = (df - df.mean()) / df.std()
D) 1. scaler = cuml.preprocessing.StandardScaler() 2. df = scaler.fit_transform(df)

2. A data scientist is using NVIDIA RAPIDS to perform statistical analysis as part of exploratory data analysis (EDA) on a dataset containing millions of product reviews. They need to compute basic descriptive statistics such as mean, median, and variance efficiently.
Which of the following methods is the most appropriate for performing these calculations on GPUs?

A) Convert the dataset into a PyTorch tensor and use PyTorch's statistical methods
B) Use NumPy's statistical functions, such as numpy.mean() and numpy.var()
C) Use a traditional SQL database to compute statistics and then transfer results to the GPU
D) Use cuDF's built-in statistical functions like .mean(), .median(), and .var()

3. You are working on a data science project using NVIDIA RAPIDS on a multi-GPU system.
To ensure reproducibility and avoid software versioning conflicts, which of the following is the best approach for managing dependencies?

A) Use a Conda environment with RAPIDS-compatible versions of libraries installed using conda install
-c rapidsai -c nvidia.
B) Install all required packages globally on the system using pip install without a virtual environment.
C) Use a manually compiled CUDA installation alongside system-installed Python libraries to manage GPU dependencies.
D) Avoid dependency management frameworks and rely on manual tracking of package versions using a text file.

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

5. You are a data scientist working on a large-scale deep learning project that requires significant computational resources. You have the option to run your workloads on a cloud-based GPU instance.
Which of the following statements best describes a key benefit of using cloud-based GPUs for your workload?

A) Cloud-based GPUs are always more cost-effective than on-premise GPUs, regardless of workload size and duration.
B) Cloud-based GPUs eliminate all data transfer bottlenecks and latencies when training models on large datasets.
C) Cloud-based GPUs enable scalable resource allocation, allowing you to dynamically increase or decrease GPU instances as needed.
D) Cloud-based GPUs provide consistent and predictable performance, identical to on-premise dedicated GPUs.

Solutions: