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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A financial institution is training a fraud detection model on a GPU-powered NVIDIA RAPIDS cuML pipeline. Their dataset includes customer ages, transaction amounts, merchant names, and transaction timestamps.
To optimize GPU memory usage while preserving accuracy, how should they store these features?

A) Store transaction amounts as float32, customer ages as int8, merchant names as categorical, and timestamps as datetime64.
B) Convert timestamps into UNIX epoch integers instead of using datetime64 for more efficient GPU computations.
C) Convert all numeric data to float64 for maximum precision, as rounding errors in lower precision could impact the model's accuracy.
D) Keep customer ages in float16 format to reduce memory consumption, as integer types are less efficient in GPU operations.

2. A machine learning team is handling large-scale datasets that need to be efficiently stored and accessed within an NVIDIA RAPIDS workflow.
Which of the following storage formats and techniques provides the best performance for GPU-based data science pipelines?

A) Use CSV files for storage, as they are widely compatible and can be read quickly using cuDF's read_csv() method.
B) Store data in the Apache Parquet format and load it directly into cuDF using cuDF's read_parquet() method.
C) Store data in SQLite databases and query it into pandas before converting it to cuDF.
D) Save data in JSON format to ensure hierarchical relationships and flexibility before loading it into cuDF.

3. A financial services company is using NVIDIA RAPIDS cuML to train a credit risk assessment model.
The dataset contains hundreds of numerical and categorical features, including loan amount, credit score, income, employment history, and previous loan defaults.
To optimize feature selection using NVIDIA technologies, which approach should they take?

A) Use cuML's feature selection algorithms, such as Recursive Feature Elimination (RFE), to identify the most important predictors.
B) Keep all features in the dataset to ensure the model captures as much information as possible, even if some features are redundant.
C) Convert all numerical features into categorical variables to simplify the data and reduce the need for feature selection.
D) Avoid feature selection, as modern deep learning models are capable of automatically handling redundant and irrelevant features.

4. A data scientist is working with a large dataset for a machine learning model and wants to accelerate feature engineering using a GPU.
Which of the following approaches will provide the most significant performance boost when using GPU acceleration?

A) Reducing dataset size by randomly removing data points without considering class balance.
B) Using a single-threaded feature extraction approach to avoid overhead from parallelization.
C) Using RAPIDS cuDF and cuML libraries to perform feature transformations on a GPU.
D) Using traditional pandas DataFrames and NumPy operations optimized for CPU processing.

5. You have developed a deep learning model using TensorFlow and trained it on an NVIDIA A100 GPU. The model is deployed in production and serves real-time inference requests. However, the inference latency is high, and you need to optimize performance without retraining the model.
Which of the following approaches is the most effective for optimizing inference performance using NVIDIA technologies?

A) Reduce the batch size to decrease computational overhead and improve latency.
B) Implement data augmentation techniques to improve inference efficiency.
C) Convert the model to ONNX format and use TensorRT for inference optimization.
D) Enable mixed precision training and retrain the model to improve inference speed.

Solutions: