NVIDIA NVIDIA-Certified Professional NCP-ADS

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist wants to compare the performance of two different GPU-accelerated data science frameworks, NVIDIA RAPIDS (cuDF, cuML) and TensorFlow, for a tabular data classification task.
Which of the following approaches would be the best practice for designing an unbiased and effective benchmark?

A) Run all benchmarks on a CPU to ensure fairness across frameworks.
B) Ignore preprocessing and focus only on model training speed when comparing performance.
C) Use TensorFlow's built-in training time metrics without comparing equivalent RAPIDS-based operations.
D) Measure execution time and memory usage for each framework using NVIDIA Nsight Systems (nsys).

2. A machine learning engineer is working on a dataset with thousands of numerical features. The dataset is too large for standard CPU-based processing, so the engineer decides to leverage GPUs for efficient feature engineering.
Which of the following techniques is the most suitable for dimensionality reduction using GPU acceleration?

A) Using manual feature selection by dropping columns with low variance on a CPU.
B) Encoding all numerical features as categorical variables to reduce dimensionality.
C) Converting the dataset into a sparse matrix and running traditional singular value decomposition (SVD) on a CPU.
D) Applying RAPIDS cuML's PCA (Principal Component Analysis) to reduce feature dimensions efficiently.

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

4. You are working with a large dataset containing missing values, and you need to clean and preprocess the data efficiently.
Which of the following methods provides the best performance when handling missing values in a GPU- accelerated EDA workflow using RAPIDS?

A) Drop all missing values using df.dropna() in Pandas before using RAPIDS.
B) Use pandas.DataFrame.fillna() on the dataset before converting it to a cuDF DataFrame.
C) Ignore missing values since GPU acceleration can handle incomplete data without performance degradation.
D) Convert the dataset to a cuDF DataFrame and use cudf.DataFrame.fillna() to fill missing values.

5. You are a data scientist working on a large-scale deep learning project. Your team needs to train a deep neural network (DNN) on terabytes of image data using NVIDIA GPUs in the cloud.
Which of the following approaches will maximize GPU utilization and optimize training time in an NVIDIA- accelerated cloud environment?

A) Avoid using mixed precision training and rely solely on full-precision (FP32) computations for numerical stability.
B) Use Jupyter Notebooks on a local machine with minimal GPU resources and transfer model checkpoints manually to the cloud for training.
C) Use NVIDIA NGC containers with TensorFlow or PyTorch and enable NCCL for multi-GPU communication.
D) Select low-cost, consumer-grade GPUs available in the cloud marketplace to reduce costs.

Solutions: