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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which feature of NVIDIA MLFlow integration with Triton Inference Server allows for the seamless deployment and monitoring of models in production?

A) MLFlow prevents models from being updated once deployed to production.
B) MLFlow only supports deployment on CPUs.
C) MLFlow handles only the training and not the deployment of models.
D) MLFlow allows for version control and automated model rollout.

2. You are comparing the performance of GPU-accelerated deep learning models on two cloud platforms: AWS EC2 and Google Cloud Platform (GCP). You want to design a benchmark that evaluates GPU resource utilization, processing time, and cost-efficiency for training models with large datasets.
Which actions should you take to implement an effective benchmark? (Select two)

A) Measure both GPU utilization and network latency between the cloud platform and your data storage location.
B) Run each model on the same GPU type and instance size across both cloud platforms.
C) Limit the benchmarking tests to a single model architecture for each cloud platform.
D) Only consider the cost of GPU resources for each cloud provider without factoring in data storage and transfer costs.

3. A data scientist is analyzing large-scale sensor readings from an industrial IoT system and wants to visualize high-frequency time-series data efficiently.
Which approach using NVIDIA technologies would be the most effective for interactive visualization of this dataset?

A) Use NVIDIA OptiX to ray-trace the time-series dataset for visualization.
B) Apply TensorRT to compress the time-series data and use PyTorch's native visualization functions.
C) Process the data using RAPIDS cuDF, then convert it to CSV and visualize it with Excel charts.
D) Use RAPIDS cuDF with Datashader to render large time-series data on the GPU efficiently.

4. A data scientist is working on a dataset where the numerical features have different ranges, and they need to ensure uniformity across features before training a machine learning model.
Which of the following approaches, utilizing NVIDIA technologies, would best achieve this goal?

A) Apply cuML's RobustScaler() to center the data using median and scale using the interquartile range.
B) Use cuML's StandardScaler() to transform the features to have zero mean and unit variance.
C) Apply cuDF's normalize() function to scale each feature between 0 and 1.
D) Use cuML's PCA to directly remove the need for standardization by reducing dimensionality.

5. You are processing a large dataset with UNIX timestamps (seconds since Jan 1, 1970) ranging from Jan 1, 2000, to the present.
What is the most memory-efficient data type for the timestamp column in a GPU-accelerated cloud environment?

A) df['timestamp'] = df['timestamp'].astype('datetime64[ms]')
B) df['timestamp'] = df['timestamp'].astype('int64')
C) df['timestamp'] = df['timestamp'].astype('int32')
D) df['timestamp'] = df['timestamp'].astype('float32')

Solutions: