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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which NVIDIA technology is specifically designed for accelerating deep learning workloads in the cloud?

A) NVIDIA Tesla
B) TensorRT
C) NVIDIA Jetson
D) NVIDIA A100

2. A company is deploying an MLOps pipeline for training and serving deep learning models. The data scientists want to leverage GPU acceleration at multiple stages of the pipeline to enhance efficiency.
Which of the following steps would benefit the most from GPU acceleration?

A) Model monitoring by logging metadata and performance metrics in a database.
B) Training and inference workloads using deep learning models with TensorFlow or PyTorch.
C) Running CI/CD workflows for code integration and deployment using a traditional CPU-based Jenkins setup.
D) Storing and retrieving models from a centralized object storage system.

3. You are working with a large dataset in a cloud environment for a deep learning model. The dataset consists of several features including numerical values, categorical data, and timestamps.
Which of the following choices would result in the most efficient use of GPU and cloud resources when determining the optimal data type for each feature? (Select three)

A) Use object data types for categorical features to avoid type conversion overhead.
B) Use float64 for all numerical features to ensure maximum precision.
C) Use int32 for all numerical features to save memory.
D) Use datetime64[ns] for timestamp features to ensure high precision.
E) Use int8 for categorical features where there are fewer than 256 categories.

4. You are working with a large dataset that contains missing values in multiple columns. Your goal is to prepare this dataset for training a machine learning model on an NVIDIA GPU using RAPIDS.
Which of the following approaches is the most efficient method to handle missing values in this scenario?

A) Drop all rows containing missing values using Pandas before transferring data to the GPU
B) Apply a deep learning-based imputation model before moving data to the GPU
C) Convert the dataset to a NumPy array and manually replace missing values with the mean
D) Use fillna() with a fixed value on the GPU using cuDF

5. You are developing an accelerated ETL workflow that requires data transformations such as filtering, aggregating, and joining large datasets. You decide to leverage NVIDIA GPUs to accelerate the transformation phase of your ETL pipeline.
Which of the following approaches will provide the greatest performance improvements when working with large-scale tabular datasets?

A) Using RAPIDS cuDF to perform transformations on a GPU
B) Using TensorFlow for data transformation tasks
C) Relying on traditional pandas for in-memory transformations
D) Performing transformations using SQL-based queries on CPU

Solutions: