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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are using cuGraph to run the PageRank algorithm on a directed web graph. The dataset is large, and you want to ensure an accurate and efficient computation while optimizing GPU performance.
Which of the following configurations is the best approach for running PageRank in cuGraph?

A) Convert the graph into an adjacency matrix and perform matrix multiplication iteratively for convergence
B) Load the graph into NetworkX first, compute PageRank, and then convert the results back into cuGraph format
C) Use the cugraph.pagerank() function with a damping factor of 0 and 10 iterations
D) Run cugraph.pagerank() with a damping factor of 0.85 and set the max iterations to 100 with a convergence threshold

2. A data science team wants to deploy a GPU-accelerated pipeline using cuGraph to analyze graph data on cloud infrastructure. They are evaluating different cloud-based GPU solutions.
Which of the following factors should they consider when selecting a cloud-based GPU instance for running cuGraph efficiently?

A) Cloud-based GPUs are only useful for rendering graphics, not for running cuGraph algorithms.
B) The choice of GPU instance does not affect cuGraph performance since all GPUs execute graph algorithms at the same speed.
C) cuGraph runs equally well on CPU-based virtual machines, making GPU instances unnecessary.
D) The availability of NVIDIA CUDA-enabled GPUs, as cuGraph requires CUDA for acceleration.

3. You are a data scientist working with a large dataset containing millions of records. You want to perform exploratory data analysis (EDA) efficiently using NVIDIA RAPIDS on a GPU-accelerated system.
Which of the following approaches is the most efficient way to handle large-scale EDA using RAPIDS?

A) Perform all EDA using NumPy and SciPy for optimized array computations.
B) Use Pandas directly for data manipulation and visualization.
C) Convert the dataset into a cuDF DataFrame and perform operations like .describe() and
.value_counts() on the GPU.
D) Load the dataset into an Apache Spark DataFrame and run .show() to inspect the data.

4. You have trained a machine learning model using cuML as part of the Modeling phase in the CRISP- DM framework. Now, you need to assess how well the model performs before moving forward with deployment.
Which of the following steps aligns best with the Evaluation phase of CRISP-DM using NVIDIA technologies?

A) Deploy the model to an edge device using TensorRT for real-time inference.
B) Define the problem statement and collect relevant datasets before training the model.
C) Compute model accuracy, precision, and recall using cuml.metrics.accuracy_score() and cuml.metrics.classification_report().
D) Optimize the data pipeline using cudf.DataFrame.merge() to improve data loading speed.

5. You are working with a large dataset that contains missing values in multiple columns. Your goal is to prepare this dataset for training a machine learning model on an NVIDIA GPU using RAPIDS.
Which of the following approaches is the most efficient method to handle missing values in this scenario?

A) Drop all rows containing missing values using Pandas before transferring data to the GPU
B) Use fillna() with a fixed value on the GPU using cuDF
C) Convert the dataset to a NumPy array and manually replace missing values with the mean
D) Apply a deep learning-based imputation model before moving data to the GPU

Solutions: