NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which of the following tools or techniques are essential for effectively working with large-scale data in a distributed environment? (Select two)

A) Using SQLAlchemy to interact with databases for large data processing
B) Using Dask for parallel processing of large datasets
C) Using SQLite as a local database for large-scale data analysis
D) Using Apache Spark for distributed data processing
E) Using Excel to manipulate large datasets

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

A) Use Dask on a single GPU machine with no GPU-specific optimization, treating the system as if it were CPU-only.
B) Use Dask with the dask_cuda package to distribute computation across GPUs, ensuring that each GPU is responsible for a portion of the data, while using distributed.Client to connect the GPU workers.
C) Use Dask with the dask_gpu package to assign data chunks across GPUs manually, without utilizing the distributed.Client.
D) Use Dask with a single GPU, distributing data across multiple workers within the GPU without considering GPU memory limitations.

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

4. You are processing a multi-terabyte dataset in CuDF and want to optimize query performance and storage efficiency.
Which approach should you follow to ensure that the dataset remains efficiently partitioned and easily accessible?

A) Use pandas for large-scale processing instead of CuDF since pandas is more stable for big data processing.
B) Store the entire dataset in a single large file to minimize the number of files in the directory.
C) Split the dataset into multiple files based on a logical partitioning key, such as a date column.
D) Convert all numeric columns to float64 for higher precision, even if float32 is sufficient.

5. A machine learning engineer is working on a multi-GPU workload using Dask and RAPIDS to process a massive dataset efficiently. However, they notice that GPU utilization is not optimal, and data transfer between GPUs is slowing down computation.
What is the best approach to minimize data transfer overhead and maximize parallel efficiency?

A) Use NVLink to enable high-bandwidth inter-GPU communication and dask_cuda.LocalCUDACluster
B) Use Dask's single-threaded scheduler to avoid multi-threading overhead
C) Transfer data between GPUs using NumPy's .copy() function
D) Load the dataset into a Pandas DataFrame and distribute computations across GPUs manually

Solutions: