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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

2. You are working on a machine learning project that requires training a large XGBoost model on a dataset containing millions of records. Due to the dataset size, training on a CPU-based environment takes an excessively long time. To accelerate the training process, you decide to use NVIDIA RAPIDS.
Which of the following is the best approach to leverage GPU acceleration for training the XGBoost model?

A) Use cuML to replace scikit-learn's XGBoost implementation, as cuML supports GPU-accelerated XGBoost training.
B) Install and use dask-xgboost, which automatically optimizes XGBoost training using GPU acceleration.
C) Store the dataset in Apache Parquet format and load it using pandas to improve training performance.
D) Use XGBoost with the "tree_method": "gpu_hist" parameter to enable GPU acceleration.

3. You are training a deep learning model for image classification and want to optimize its hyperparameters, including learning rate, batch size, and number of layers.
Which of the following techniques is the most effective for efficiently searching through a high- dimensional hyperparameter space?

A) Gradient Descent
B) Bayesian Optimization
C) Grid Search
D) Random Search

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

5. A data scientist is using NVIDIA RAPIDS cuDF to process a large dataset of customer transactions.
The dataset contains numerical, categorical, and timestamp-based features.
To optimize memory usage and performance on NVIDIA GPUs, which approach should they take when selecting data types?

A) Convert all timestamp features into object (string) format to maintain readability and ensure compatibility with GPU processing.
B) Convert categorical variables into cuDF categorical data types and downcast numerical columns to the smallest possible precision without losing information.
C) Store all numerical columns as float64 to preserve maximum precision, even if lower precision suffices.
D) Avoid downcasting integer columns, as lower-bit integer types (e.g., int8) are not supported in GPU- accelerated computations.

Solutions: