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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which of the following can DLProf specifically help identify when profiling a deep learning model on Nvidia GPUs?

A) Training dataset bias.
B) GPU utilization and memory usage.
C) Number of model parameters.
D) Hyperparameter tuning results.

2. You are designing an ETL workflow to process large-scale financial transaction data using GPU acceleration. The dataset is stored in a Parquet file and contains millions of records.
Which of the following approaches is the most efficient for performing extract, transform, and load (ETL) operations using NVIDIA RAPIDS technologies?

A) Use Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics.
B) Store all data as CSV files and perform ETL operations using traditional row-based processing.
C) Load the Parquet file directly into a cuDF DataFrame and use cuDF's built-in functions for transformations before writing the results back to storage.
D) Use Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.

3. 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)

A) Reduce the number of GPUs used and focus on fine-tuning the hyperparameters for optimal performance on a single GPU.
B) Ensure that the model is distributed evenly across GPUs to prevent some GPUs from being underutilized.
C) Disable gradient accumulation when using multi-GPU setups to increase communication efficiency.
D) Ensure efficient multi-GPU communication and synchronization strategies, such as using NCCL for distributed training.
E) Increase the batch size to better utilize the multiple GPUs and reduce the number of updates to the model during training.

4. 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?

A) Vaex
B) Dask
C) Pandas
D) PySpark

5. 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?

A) cuml.preprocessing.LabelEncoder()
B) cuml.preprocessing.MinMaxScaler()
C) cuml.preprocessing.OneHotEncoder()
D) cuml.preprocessing.StandardScaler()

Solutions: