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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is working with large-scale datasets in a RAPIDS AI pipeline and needs to efficiently process and organize the data while leveraging GPU acceleration.
Which of the following approaches best ensures optimized processing and memory management when using NVIDIA technologies?

A) Process data using Apache Spark on CPU before transferring results to GPU for model training.
B) Write intermediate data to disk as CSV files before reading them back into RAPIDS AI to avoid memory overflow.
C) Use cuDF to store and process data in GPU memory, leveraging its vectorized operations for transformations and aggregations.
D) Store data in a pandas DataFrame first and then convert it to cuDF only when GPU operations are needed.

2. You are working on a machine learning problem that involves training a deep learning model on a dataset with billions of records. The dataset is stored in a distributed cloud storage system.
Given the need for acceleration, which is the most effective approach?

A) Use GPU acceleration with libraries like RAPIDS AI or TensorFlow to leverage parallel processing.
B) Store the dataset in a relational database and query it sequentially using SQL before training the model.
C) Reduce the dataset to a small representative sample to avoid the need for specialized acceleration.
D) Load the entire dataset into RAM on a single powerful CPU-based machine before starting model training.

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

4. You are tasked with profiling a PyTorch-based deep learning model to identify performance bottlenecks using NVIDIA DLProf. Your goal is to analyze kernel execution times and identify operations causing excessive memory consumption.
Which of the following steps is the MOST appropriate sequence for profiling using DLProf?

A) Run dlprof --mode=default --output_path=profile_results on the training script, analyze the generated report, and optimize memory-intensive operations.
B) Profile the model using torch.profiler, then compare the results against the DLProf report to analyze GPU-specific kernel optimizations.
C) Use nvidia-smi to capture GPU utilization metrics, then manually correlate high utilization periods with the training script to determine bottlenecks.
D) Execute the training script under DLProf TensorBoard mode to visualize performance insights, then re-run the model with automatic mixed precision (AMP) to reduce memory usage.

5. You have a structured dataset containing 20 million records with missing values in several columns.
You need to fill missing values while ensuring that the approach is optimal for execution on NVIDIA GPUs.
Which method should you use?

A) Use cuDF's .fillna() method to replace missing values in GPU memory
B) Drop all missing values using .dropna() instead of filling them, as GPU memory is limited
C) Convert the dataset to a pandas DataFrame, fill missing values, and then convert it back to cuDF
D) Load the dataset into Modin with a Dask backend and use .fillna() for parallel execution

Solutions: