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1. A data scientist is using NVIDIA RAPIDS to perform statistical analysis as part of exploratory data analysis (EDA) on a dataset containing millions of product reviews. They need to compute basic descriptive statistics such as mean, median, and variance efficiently.

Which of the following methods is the most appropriate for performing these calculations on GPUs?

2. You are using RAPIDS and Dask-cuDF to process a large-scale ETL pipeline. The workflow involves multiple join and groupby operations, which are causing excessive shuffling.

How can you best optimize caching to reduce shuffle overhead?

3. You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.

Which of the following frameworks would be the best choice for handling this workload?

4. A data science team wants to deploy a GPU-accelerated pipeline using cuGraph to analyze graph data on cloud infrastructure. They are evaluating different cloud-based GPU solutions.

Which of the following factors should they consider when selecting a cloud-based GPU instance for running cuGraph efficiently?

5. A data engineer is designing an Extract, Transform, Load (ETL) pipeline for a retail analytics platform that processes millions of customer transactions per day. The primary objective is to accelerate data ingestion, transformation, and storage while ensuring efficient scalability.

Which of the following approaches would be the most effective for optimizing this ETL workflow using NVIDIA-accelerated ETL tools?

6. 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?

7. You are building a predictive model for retail sales forecasting and need a dataset that includes historical sales transactions, customer demographics, and external economic indicators (e.g., inflation rate, unemployment rate).

Which of the following datasets would be the most appropriate for your model?

8. A data scientist is working with a large dataset containing missing values and outliers. The dataset will be used for training a machine learning model. The scientist decides to preprocess the data using RAPIDS cuDF, an accelerated dataframe library.

Which of the following is the most efficient approach to handle missing values while maintaining data integrity?

9. A data engineering team is designing an ETL pipeline to process large-scale financial transaction data. They want to leverage NVIDIA-accelerated ETL tools to extract data from a data lake, transform it by filtering and aggregating key fields, and load it into a data warehouse.

Which of the following approaches provides the most efficient ETL processing using NVIDIA technologies?

10. You have a large-scale dataset consisting of IoT sensor readings collected at one-minute intervals across multiple locations. The dataset contains missing values and requires scaling before applying a machine learning model. You plan to use NVIDIA RAPIDS to preprocess and analyze the time-series data efficiently on GPUs.

Which of the following preprocessing steps is the most efficient approach using NVIDIA RAPIDS?

11. Which of the following is the most efficient way to implement data parallelism using Dask for multi-GPU scaling on an Nvidia platform?

12. A data scientist is preprocessing a dataset containing multiple categorical features using NVIDIA RAPIDS to accelerate feature engineering.

The dataset contains:

A low-cardinality categorical feature (Product Type) with 10 unique values.

A high-cardinality categorical feature (User ID) with 100,000 unique values.

A numerical feature (Price) that requires transformation.

Which of the following feature engineering approaches will be the most efficient for GPU acceleration?

13. You are analyzing a large financial dataset containing stock market tick-by-tick data stored in a cuDF DataFrame. Since the dataset contains billions of data points, you need to aggregate it at the minute level before visualizing price trends efficiently.

Which of the following is the best approach for aggregating and visualizing this time-series data using NVIDIA technologies?

14. 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?

15. Which of the following steps is the first in the CRISP-DM (Cross-Industry Standard Process for Data Mining) process when using NVIDIA technologies?

16. You are a data scientist analyzing a social media network with NVIDIA cuGraph to identify the most influential users using the PageRank algorithm.

Which option best describes how cuGraph PageRank operates on a directed graph?

17. You are working with structured tabular data in a cloud-based GPU environment.

Your dataset contains the following columns:

Column Name Example Values Data Type Needed

user_id 15432, 98765, 43210 Integer

purchase_amt 12.99, 35.50, 100.75 Floating Point

category 'Books', 'Electronics' Categorical

Which of the following is the most optimal approach to assign data types to these columns to ensure efficient memory usage and computational performance?

18. You are tasked with implementing data caching to reduce shuffle in an accelerated machine learning pipeline using NVIDIA technologies. You need to cache intermediate results after a shuffle operation in a distributed setting.

Which of the following is the best approach to minimize shuffle overhead and maximize performance?

19. You are training a deep learning model on a large dataset of images stored in an Amazon S3 bucket. You want to optimize data loading, augmentation, and preprocessing on NVIDIA GPUs to avoid CPU bottlenecks.

Which of the following approaches is the most efficient for GPU-accelerated data preprocessing?

20. You are designing an accelerated ETL pipeline to process large-scale datasets in a data science workflow.

Which of the following are key considerations when selecting the right tools and methods for implementing this pipeline? (Select two)

21. A data scientist is working on training a deep learning model in a cloud-based environment. The dataset is large, and model convergence is taking too long on a standard CPU instance.

To optimize performance through GPU acceleration, which of the following strategies should the data scientist implement?

22. You are analyzing a large-scale transportation network using cuGraph and notice that query times are longer than expected when running graph algorithms.

What is the best way to optimize graph processing performance using GPU-accelerated tools?

23. A data scientist needs to process a dataset containing 10 million records, performing transformations and exploratory data analysis (EDA). The processing needs to be efficient but does not require high-performance multi-GPU execution.

Which of the following libraries provides the best balance between usability and performance?

24. You are optimizing a data pipeline for a large-scale machine learning project using NVIDIA RAPIDS and Apache Spark. The pipeline performs many expensive shuffle operations.

Which of the following is the most effective method to reduce shuffle and improve performance using NVIDIA technologies?

25. You have a massive time-series dataset containing millions of records per day, and you need to perform forecasting at scale.

Which of the following techniques best utilizes NVIDIA technologies to optimize time-series forecasting?

26. You are designing a machine learning pipeline and must decide whether your dataset qualifies as "big data" and requires specialized acceleration methods.

Which of the following characteristics best indicates that your dataset meets the definition of big data?

27. You are working on a time-series forecasting project using NVIDIA RAPIDS and GPU-accelerated machine learning. The dataset consists of 10 years of daily stock price data. Your goal is to implement a model that efficiently handles large-scale time-series data while leveraging GPU acceleration for optimal performance.

Which approach best utilizes NVIDIA technologies for efficient forecasting?

28. A data scientist is working with a large dataset that contains string-based numeric values that need to be converted to floating-point numbers for further analysis. The dataset is stored as a cuDF DataFrame, and the scientist needs to ensure the conversion is performed optimally on a GPU.

Which of the following is the best method for converting string-based numeric values to floating-point numbers using NVIDIA-accelerated processing?

29. 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?

30. You are working with a cuDF DataFrame and need to convert a column named sales from float64 to int32 to save memory.

Which of the following is the correct and most efficient way to perform this conversion in cuDF?

31. A data scientist is working with large-scale ETL (Extract, Transform, Load) pipelines on GPU-accelerated infrastructure using RAPIDS. The workload involves frequent shuffle operations, which significantly impact performance.

What is the best approach using NVIDIA technologies to reduce shuffle overhead and improve performance?

32. You are tasked with optimizing the performance of a large-scale data science project that involves deep learning models on a cloud infrastructure. Your organization is using GPUs for model training.

Which of the following strategies would be the most effective in optimizing GPU performance for data science tasks? (Select two)

33. You are working with a large dataset on an NVIDIA GPU, where optimizing memory usage is a priority. Your dataset contains a column, transaction_id, which stores unique integer values ranging between 0 and 100,000.

Which of the following data types is the most memory-efficient choice for this column in cuDF?

34. 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?

35. You are working on a dataset containing missing values, duplicate records, and inconsistent data types.

The dataset size is 15GB and you need to efficiently perform data cleansing operations such as:

- Handling missing values

- Dropping duplicates

- Converting data types

Which of the following approaches would be the most efficient way to perform these operations on an NVIDIA GPU?

36. A machine learning engineer is training a convolutional neural network (CNN) on an NVIDIA GPU and needs to maximize throughput while avoiding OOM errors.

Which of the following techniques is the most effective way to balance memory efficiency and training speed?

37. A financial analyst wants to create an interactive GPU-accelerated dashboard to visualize stock price movements in real-time.

Which NVIDIA-supported tool is best suited for this purpose?

38. You are working with a dataset in a cloud-based GPU environment that contains a column country representing the country of origin for customers. The column contains only 10 unique country values, but the dataset has millions of rows.

Which of the following is the most memory-efficient approach to handle the country column in a cuDF DataFrame?

39. You are working on optimizing a deep learning model for inference on an NVIDIA GPU. You decide to use NVIDIA DLProf to profile the model and analyze its performance. After running DLProf, you review the generated reports and find that the GPU Utilization is significantly lower than expected.

Which of the following is the most likely reason for this issue, as indicated by the profiling data?

40. You are building a large-scale AI training pipeline that requires efficient storage and retrieval of

structured and unstructured datasets across multiple GPUs.

Which of the following is the best NVIDIA technology to organize and manage datasets at scale?