Free NVIDIA NCP-ADS Exam Dumps Questions & Answers

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

A machine learning engineer is working on an image classification problem where the dataset is small and lacks variability. To improve generalization, the engineer decides to augment the dataset using NVIDIA RAPIDS.
What is the best method to generate synthetic data efficiently while leveraging GPU acceleration?

• A.Apply cuML.GaussianMixture() to generate new synthetic data points based on an estimated probability distribution.
• B.Use cuML.PCA() to reduce dimensionality and create synthetic samples by reconstructing the data with added noise.
• C.Use cuDF with cudf.DataFrame.sample() to create new samples by randomly selecting existing rows.
• D.Use traditional CPU-based augmentation techniques like OpenCV to transform images and generate new data.

You are building an MLOps pipeline for a predictive model that uses tabular data with both categorical and numerical features.
To ensure efficient data processing and optimal model training on an NVIDIA GPU, which of the following data types would be most suitable for a categorical feature representing different product categories?

• A.String
• B.Int32
• C.Float64
• D.Int64

You are a data scientist working on a large-scale deep learning project that requires significant computational resources. You have the option to run your workloads on a cloud-based GPU instance.
Which of the following statements best describes a key benefit of using cloud-based GPUs for your workload?

• A.Cloud-based GPUs are always more cost-effective than on-premise GPUs, regardless of workload size and duration.
• B.Cloud-based GPUs eliminate all data transfer bottlenecks and latencies when training models on large datasets.
• C.Cloud-based GPUs provide consistent and predictable performance, identical to on-premise dedicated GPUs.
• D.Cloud-based GPUs enable scalable resource allocation, allowing you to dynamically increase or decrease GPU instances as needed.

You are analyzing a dataset that contains missing values.
Which of the following techniques is most appropriate when dealing with missing numerical data in a dataset, ensuring minimal impact on model performance?

• A.Using k-nearest neighbors (KNN) imputation
• B.Replacing missing values with a constant value (e.g., zero)
• C.Replacing missing values with the mean of the column
• D.Removing rows with missing values