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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a data science project using NVIDIA RAPIDS on a multi-GPU system.
To ensure reproducibility and avoid software versioning conflicts, which of the following is the best approach for managing dependencies?

A) Use a Conda environment with RAPIDS-compatible versions of libraries installed using conda install
-c rapidsai -c nvidia.
B) Install all required packages globally on the system using pip install without a virtual environment.
C) Use a manually compiled CUDA installation alongside system-installed Python libraries to manage GPU dependencies.
D) Avoid dependency management frameworks and rely on manual tracking of package versions using a text file.

2. You are working with a large dataset in RAPIDS cuDF and plan to standardize the numerical features using cuml.preprocessing.StandardScaler(). However, some columns contain missing values.
What is the best approach to handle the missing values before applying standardization?

A) cuml.impute.KNNImputer() to replace missing values based on k-nearest neighbors.
B) Use cudf.DataFrame.fillna(method='ffill') to forward-fill missing values.
C) Use cuml.impute.SimpleImputer(strategy='mean') to replace missing values with the column mean.
D) Replace missing values with zero before standardization.

3. You are implementing a Dask-based solution for distributed data parallelism across a multi-GPU system.
Which configuration steps would ensure effective use of GPUs for parallel computation? (Select two)

A) Use dask_cuda's LocalCUDACluster with proper GPU memory management to handle multiple GPUs
B) Create a LocalCUDACluster and manually specify the GPUs you want to use for each Dask worker
C) Use dask_cuda's LocalCUDACluster and let Dask automatically allocate GPUs without any configuration
D) Use dask_cudf to convert DataFrame computations into GPU-accelerated operations using cuDF
E) Use Dask's Cluster class with the distributed scheduler and specify CPU cores only for GPU workloads

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

A) GPUs are designed primarily for rendering graphics and have limited utility in machine learning and deep learning applications.
B) GPUs are optimized for sequential data processing tasks, making them more efficient than CPUs for database operations.
C) GPUs are only effective for acceleration when used in conjunction with Tensor Processing Units (TPUs), as they cannot train deep learning models independently.
D) GPUs use thousands of smaller cores that can execute many parallel computations simultaneously, making them ideal for large-scale matrix operations.

5. 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?

A) A dataset with global temperature trends over the past decade
B) A dataset containing transaction history and customer profiles from a retail company
C) A dataset of product reviews and customer sentiments from an e-commerce website
D) A public dataset of annual GDP per country

Solutions: