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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. 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) Ensure that the model is distributed evenly across GPUs to prevent some GPUs from being underutilized.
B) Disable gradient accumulation when using multi-GPU setups to increase communication efficiency.
C) Reduce the number of GPUs used and focus on fine-tuning the hyperparameters for optimal performance on a single GPU.
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.

2. A machine learning engineer is training a large transformer-based model for natural language processing (NLP). They want to maximize training speed and efficiency using NVIDIA GPUs.
Which of the following techniques would most effectively enhance GPU utilization and reduce training time?

A) Prefetching data with the CPU while training on the GPU
B) Using mixed-precision training with Tensor Cores
C) Running training exclusively on CPU
D) Disabling data parallelism

3. 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?

A) Use Dask for distributed missing value imputation and train a model using TensorFlow's CPU-based estimator.
B) Use cuDF to handle missing values with GPU-accelerated interpolation and apply cuML's StandardScaler for feature scaling.
C) Use pandas to fill missing values and scale the data, then convert it to cuDF for training.
D) Use pandas for missing value imputation, then normalize the data using NumPy before converting to cuDF.

4. 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?

A) Use Dask on CPUs for distributed ETL processing and later move results to a GPU-based database
B) Write a custom ETL script in pure Python to handle data extraction, transformation, and loading
C) Use RAPIDS cuDF to preprocess data in-memory and BlazingSQL to accelerate SQL-based transformations
D) Perform all transformations using Pandas DataFrames before loading the data into the GPU

5. You are working with a large dataset in NVIDIA RAPIDS cuDF and notice that the data processing pipeline is taking longer than expected.
Which of the following tools or techniques can help you identify and analyze bottlenecks in the pipeline?

A) Use cuml.preprocessing.StandardScaler() to normalize the dataset before processing.
B) Enable pandas compatibility mode in cudf to improve performance.
C) Use cudf.DataFrame.astype(str) to convert numerical values to strings for easier debugging.
D) Use nvtx.annotate() to label sections of code and analyze performance in Nsight Systems.

Solutions: