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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are running a data science project on a cloud environment, where you need to optimize the GPU utilization for real-time data processing tasks.
Which of the following practices should you consider to maximize GPU performance? (Select two)

A) Use NVIDIA CUDA libraries optimized for data science tasks to accelerate computations.
B) Use data parallelism techniques to split the workload evenly across multiple GPUs.
C) Prioritize using CPU for computationally expensive tasks and reserve GPU for I/O operations.
D) Implement model inference in a batch mode rather than in a real-time streaming mode for better performance.
E) Scale the cloud infrastructure vertically by increasing the size of the GPU in use.

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

3. You are working on a dataset containing missing values, duplicate records, and inconsistent data types.
The dataset size is 15GB and you need to efficiently perform data cleansing operations such as:
- Handling missing values
- Dropping duplicates
- Converting data types
Which of the following approaches would be the most efficient way to perform these operations on an NVIDIA GPU?

A) Load the dataset using cuDF, then use cuDF's built-in .dropna(), .drop_duplicates(), and .astype() methods
B) Use pandas to load the dataset and perform the operations using standard pandas functions
C) Use Vaex to perform data cleansing, as it is optimized for large-scale datasets
D) Convert the dataset into a NumPy array and process it using CuPy before converting it back to a DataFrame

4. You are tasked with comparing the performance of different GPU-accelerated frameworks for a deep learning model. The frameworks you are considering are TensorFlow, PyTorch, and CUDA. To evaluate the performance, you decide to implement a benchmark that measures GPU efficiency, memory usage, and speed.
Which of the following actions should you take to design an effective benchmark? (Select two)

A) Use a batch size that is optimal for each framework's memory management.
B) Measure GPU utilization and memory usage, but ignore the network and disk I/O.
C) Use CPU-based implementations of the same frameworks for comparison.
D) Use a common dataset for all frameworks to ensure comparability.
E) Benchmark only the training phase of the deep learning model.

5. Which of the following best describes the role of MLOps in the context of NVIDIA technologies for deploying machine learning models in production? (Select two)

A) MLOps frameworks support version control and automation, ensuring reproducibility and scalability of ML workflows
B) MLOps replaces the need for data preprocessing during training and deployment
C) MLOps ensures that models trained on GPUs can only run on GPUs during deployment
D) MLOps helps manage the lifecycle of machine learning models, ensuring efficient collaboration and model governance

Solutions: