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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are deploying an NVIDIA GPU-accelerated machine learning model in a Docker container and want to ensure that your application can leverage the GPU efficiently.
What is the best way to manage CUDA dependencies and avoid compatibility issues inside your Docker container?

A) Use a base Ubuntu image and install TensorFlow, PyTorch, and CUDA using pip install inside the container.
B) Use NVIDIA's official Docker images from NVIDIA GPU Cloud (NGC), which come with pre-installed CUDA and AI frameworks.
C) Disable GPU acceleration in Docker and force computations on the CPU to avoid CUDA compatibility issues.
D) Manually install CUDA and cuDNN inside the container by downloading them from NVIDIA's website and setting environment variables.

2. In Python, when working with large datasets using pandas, which of the following methods are best for improving performance and efficiency when applying operations on DataFrames? (Select two)

A) Using apply() function over DataFrame rows
B) Using vectorized operations (e.g., element-wise arithmetic)
C) Using for loops to apply operations row by row
D) Using iterrows() for iterating through DataFrame rows
E) Using map() function to apply a function element-wise

3. A machine learning engineer is tasked with deploying a real-time image classification model as part of an MLOps pipeline. The model requires low-latency inference and must handle high-throughput requests efficiently.
Which of the following deployment strategies is the most suitable for leveraging GPU acceleration?

A) Running the model inference on a multi-core CPU server with batch processing enabled.
B) Using an Apache Spark cluster with distributed CPU-based inference.
C) Using a REST API wrapper to load the model dynamically into memory for each request.
D) Deploying the model using NVIDIA Triton Inference Server with TensorRT optimizations.

4. You are using RAPIDS cuML to train a regression model on a dataset with features of varying scales (temperature in Celsius, revenue in thousands, customer age). To improve model performance, you decide to standardize the data.
Which approach correctly standardizes the data using NVIDIA technologies?

A) Use cuml.MinMaxScaler() to scale the features to a range of [0,1] without adjusting for mean and variance.
B) Use numpy.mean() and numpy.std() to manually standardize the dataset before feeding it into the GPU.
C) Use cuml.StandardScaler() to transform the features to have a mean of zero and a standard deviation of one.
D) Use cuml.PCA() to reduce the dimensionality of the dataset, which also standardizes feature variance.

5. You are designing an ETL pipeline to process terabytes of financial transaction data in real time.
The pipeline consists of:
Extracting data from multiple sources (CSV, Parquet, and SQL databases), Transforming the data using operations such as filtering, joins, and aggregations, Loading the processed data into a data lake for analytics.
Given that you are using NVIDIA RAPIDS cuDF for GPU-accelerated ETL, which of the following approaches optimizes performance while ensuring scalability?

A) Load all data into a single, large cuDF DataFrame before performing transformations
B) Use cuDF to read and process the data in batches, leveraging Dask-cuDF for distributed computation when necessary
C) Use CPU-based ETL frameworks such as Apache Spark without GPU acceleration
D) Convert cuDF DataFrames to Pandas DataFrames before performing transformations for compatibility

Solutions: