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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are training a large-scale random forest model on a dataset with millions of rows and hundreds of features. The training time is significantly high when using traditional CPU-based machine learning frameworks.
Which NVIDIA technology should you use to accelerate training while maintaining compatibility with common ML frameworks like scikit-learn?

A) NVIDIA RAPIDS cuML to accelerate random forest training using GPU-optimized implementations.
B) NVIDIA Triton Inference Server to distribute random forest model training across multiple GPUs.
C) NVIDIA TensorRT to accelerate random forest model training by optimizing tree-based algorithms.
D) NVIDIA DeepStream to preprocess tabular data and optimize random forest model execution.

2. A machine learning engineer wants to deploy a GPU-accelerated inference model in a containerized environment while ensuring compatibility with NVIDIA libraries.
Which of the following is the best approach for managing dependencies?

A) Use the official NVIDIA Docker base images (nvidia/cuda) and install RAPIDS AI libraries within the container to ensure GPU compatibility.
B) Install GPU drivers directly inside the container instead of on the host system to avoid dependency conflicts.
C) Disable the --gpus flag when running Docker containers, as RAPIDS AI libraries do not require explicit GPU selection.
D) Run Docker containers without any special configurations, as Docker automatically detects and utilizes GPUs.

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

4. You are designing an accelerated ETL pipeline to process large-scale datasets in a data science workflow.
Which of the following are key considerations when selecting the right tools and methods for implementing this pipeline? (Select two)

A) Leveraging parallel processing and distributed computing frameworks like Apache Spark to speed up the transformation phase.
B) Using a single storage location for both raw and transformed data to simplify the workflow.
C) Using GPU-accelerated libraries such as RAPIDS for data transformation to enhance processing speed.
D) Ensuring the ETL pipeline uses only batch processing for data ingestion.
E) Relying on traditional single-threaded processing for the extraction phase to reduce complexity.

5. 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) Triton Inference Server
C) DLA (Deep Learning Accelerator)
D) cuBLAS

Solutions: