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1. You are tasked with processing a large dataset of 100 million records for a deep learning project using NVIDIA technologies. You need to determine the most efficient data processing library for this task to maximize performance and reduce processing time.

Which of the following libraries is best suited for this task?

2. You need to generate synthetic data to augment an imbalanced dataset using RAPIDS™ and cuDF.

Which of the following strategies would be most effective in producing high-quality synthetic data for the minority class?

3. You are working on an MLOps pipeline that involves loading a large dataset for training a deep learning model on an NVIDIA GPU. Before training, you need to ensure that the dataset fits within the available GPU memory.

Which of the following commands in Python using the pandas and numpy libraries can correctly determine the memory size of a dataset?

4. A data scientist is training a deep learning model and wants to find the best learning rate to optimize convergence speed and generalization. The scientist tests different values: A very small learning rate (0.00001) results in slow convergence.

A very large learning rate (10) causes the model loss to fluctuate wildly and not converge.

Which of the following strategies is the most effective way to optimize the learning rate dynamically during training?

5. A financial institution is developing an ETL pipeline to ingest and process large volumes of streaming data from various sources, including stock market feeds, real-time transactions, and economic indicators. The ETL process must be highly efficient to minimize latency while ensuring data integrity.

Which of the following strategies is best suited for implementing a high-performance, GPU-accelerated ETL pipeline?

6. A data scientist is using RAPIDS cuML to build a predictive model on a large dataset containing numerical and categorical features.

To optimize feature engineering for accelerated GPU processing, which of the following is the best approach?

7. 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?

8. You are tasked with implementing a multi-GPU data pipeline using Dask-CUDA to process large datasets stored in Parquet format. Your goal is to achieve optimal GPU memory utilization and minimize inter-GPU communication overhead.

Which of the following approaches best aligns with these goals?

9. A data scientist is analyzing large-scale sensor readings from an industrial IoT system and wants to visualize high-frequency time-series data efficiently.

Which approach using NVIDIA technologies would be the most effective for interactive visualization of this dataset?

10. You need to determine the optimal data processing library for a small dataset of 500,000 records that will be processed on a multi-core CPU machine with no GPU access.

Which of the following libraries would be the most efficient for this task?

11. Which of the following scenarios are most appropriate for using GPU acceleration when working with large-scale datasets in machine learning? (Select two)

12. A data scientist is analyzing a large time-series dataset containing stock price movements of thousands of companies over a decade. The dataset is stored as a cuDF DataFrame and contains millions of rows. The scientist wants to visualize trends and patterns interactively while leveraging GPU acceleration.

Which of the following approaches is the most efficient for visualizing this time-series data?

13. You are working on a medium-sized dataset (~500,000 rows, 20 columns) and need to perform fast exploratory data analysis (EDA) with filtering, aggregations, and transformations.

Which of the following Python libraries would be the most efficient choice for this task?

14. You are working on an MLOps project where GPU-accelerated workflows are being used for model training. You want to benchmark and optimize these workflows to ensure the best performance.

Which of the following steps should you consider to effectively benchmark and optimize GPU-accelerated workflows? (Select two)

15. You are tasked with acquiring a dataset for training a machine learning model in healthcare, predicting patient readmission rates. Before using the dataset, you must assess its quality.

Which of the following is the most important factor to evaluate before acquisition?

16. You are tasked with designing an ETL workflow for a large-scale data processing pipeline using NVIDIA technologies. You need to ensure that the extraction, transformation, and loading phases are optimized for performance using hardware acceleration.

Which of the following NVIDIA technologies would be most suitable for accelerating the ETL process?

17. A financial institution is using cuGraph to analyze transaction data and detect potential fraudulent activity. The institution wants to identify users who have a high likelihood of being involved in suspicious activities based on the structure of their transactions.

Which of the following cuGraph algorithms would be the best choice for this task?

18. Which of the following methods are commonly used to handle missing data in data analysis? (Select two)

19. You are working with a large-scale social network dataset and need to analyze relationships between users to detect communities using the Louvain algorithm. Given the benefits of GPU acceleration, you decide to use cuGraph for this task.

Which of the following statements best describes why cuGraph is beneficial for this workload?

20. When deciding whether to use GPU acceleration or a traditional CPU approach for a machine learning task, which of the following factors should be considered to determine if the data qualifies as "big data" and whether GPU acceleration is beneficial? (Select two)

21. A data scientist is preprocessing a dataset containing several types of features:

A timestamp column storing millisecond-resolution timestamps.

A column with binary categorical values (Yes/No).

A column containing large continuous numerical values.

A column containing product category codes ranging from 0 to 5000.

Which of the following data type choices is the most optimal for maximizing GPU processing efficiency using NVIDIA cuDF?

22. Which of the following techniques are best suited for efficiently processing and organizing large datasets using NVIDIA technologies? (Select two)

23. 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?

24. You are working on a machine learning pipeline using NVIDIA RAPIDS cuML and need to standardize the dataset to ensure that all features have a mean of 0 and a standard deviation of 1.

Which of the following methods should you use to achieve this in cuML?

25. You are training a deep learning model on a large dataset. Initially, you train the model on a single GPU and achieve a training time of 10 hours. To speed up training, you switch to a multi-GPU setup with four GPUs. However, after testing, you notice that the training time is only reduced to 3.5 hours instead of the expected 2.5 hours (a linear speedup).

What is the most likely reason for this sublinear speedup?

26. You are considering using a multi-GPU setup to accelerate training a large deep learning model.

Which of the following are important factors to consider when deciding whether to use single-GPU or multi-GPU training? (Select two)

27. You are tasked with designing and implementing a benchmark to compare the performance of different deep learning frameworks, including TensorFlow, PyTorch, and JAX, using NVIDIA GPUs.

Which of the following is the most effective approach to ensure an accurate and fair comparison?

28. A data scientist is working on a dataset where the numerical features have different ranges, and they need to ensure uniformity across features before training a machine learning model.

Which of the following approaches, utilizing NVIDIA technologies, would best achieve this goal?

29. Which of the following statements best describes the role of GPUs in accelerating data science workloads?

30. You are monitoring a GPU-accelerated ETL pipeline using RAPIDS cuDF and Dask-cuDF. You suspect that a bottleneck is causing the pipeline to slow down.

Which of the following methods is the most effective way to diagnose performance bottlenecks in your data processing pipeline?

31. A machine learning engineer wants to evaluate the performance of NVIDIA RAPIDS cuDF and Apache Spark for large-scale data processing on a GPU-enabled cluster.

Which of the following strategies is the most effective for obtaining a fair and comprehensive benchmark?

32. You are performing data cleansing on a large dataset using CuDF. The dataset contains numerical values, some of which are outliers. You need to remove or adjust these outliers to make your model training more robust.

Which of the following approaches should you consider for handling outliers

efficiently in CuDF? (Select two)

33. A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.

Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?

34. You are tasked with optimizing the performance of an MLOps pipeline that uses GPU-accelerated workflows. After running initial benchmarks, you notice that the training time is higher than expected, despite the use of multiple GPUs.

What are the best strategies to optimize the GPU-accelerated workflow in this case? (Select two)

35. You are building a deep learning model using TensorFlow with cuDNN acceleration on an NVIDIA GPU. Your dataset contains continuous numerical features with vastly different ranges.

What is the best way to standardize the data efficiently to improve model convergence?

36. A machine learning engineer is tasked with optimizing an image classification model on a cloud platform. The engineer must select a GPU-accelerated instance that balances cost and performance while ensuring compatibility with frameworks like TensorFlow and PyTorch.

Which instance configuration is the most appropriate choice?

37. You are processing a large-scale transportation network graph using NVIDIA cuGraph. The graph is extremely large, consuming almost all available GPU memory. Performance is deteriorating, and some computations fail due to memory exhaustion.

What is the best approach to efficiently handle this large graph while keeping computations on the GPU?

38. 1.A data scientist is training a deep learning model on an NVIDIA GPU but is encountering out-of-memory (OOM) errors.

To optimize GPU memory usage while maintaining efficient training performance, which of the following strategies should they prioritize?

39. A data scientist is using an NVIDIA RAPIDS-based data processing pipeline on a GPU cluster. They notice that the pipeline is not performing as expected and suspect a bottleneck.

Which of the following approaches would best help identify the source of the bottleneck?

40. You are working on an accelerated data science project and need to acquire a large dataset stored in a Parquet file format and load it efficiently for GPU processing using NVIDIA RAPIDS.

Which of the following approaches is the most efficient way to load the dataset into a GPU-accelerated DataFrame?