Which of the following tools in the NVIDIA AI stack is specifically designed to accelerate the deployment of machine learning models for production by optimizing inference performance?

• A. cuML
• B. DLA (Deep Learning Accelerator)
• C. Triton Inference Server
• D. cuBLAS

You are processing large-scale datasets in Dask-cuDF and observe that your computation involves excessive data shuffling, which slows down performance. You decide to implement data caching to reduce shuffle overhead.
Which of the following best describes a technique to reduce shuffle costs in a Dask-cuDF workflow?

• A. Persisting intermediate results in distributed GPU memory using df.persist() ensures that Dask does not recompute partitions, reducing shuffle overhead.
• B. Disabling lazy execution using dask.config.set(lazy=False) forces all computations to execute eagerly, thereby avoiding shuffle.
• C. Using .compute() on each partition immediately forces Dask to materialize results and store them in memory, ensuring shuffle operations are avoided.
• D. Splitting the DataFrame into multiple smaller DataFrames and recomputing each separately reduces shuffle operations in Dask-cuDF.

You are working on an AI-driven customer behavior prediction project.
According to the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, what is the most critical task to complete during the Data Understanding phase?

• A. Identifying and preparing feature engineering strategies to improve model accuracy.
• B. Selecting the most appropriate machine learning algorithm for the prediction task.
• C. Acquiring and exploring the dataset to assess quality, completeness, and potential biases.
• D. Deploying the model into a production environment for real-time inference.

A machine learning team needs to process terabytes of image metadata stored in a distributed storage system. They want to leverage GPU acceleration to speed up preprocessing and transformation while ensuring efficient parallel access.
Which of the following approaches best aligns with NVIDIA's accelerated data science ecosystem?

• A. Use SQLite for metadata storage and process it with RAPIDS cuDF.
• B. Use Dask-cuDF with Parquet files stored in an object storage system like S3.
• C. Store metadata in a traditional SQL database and query it using Pandas.
• D. Process metadata using PySpark on a CPU cluster before sending it to a GPU.