NVIDIA-Certified-Professional Accelerated Data Science : NCP-ADS

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. What is the primary advantage of using NVIDIA Triton Inference Server for deploying and monitoring machine learning models in production?

A) It only supports TensorFlow models for inference.
B) It is designed solely for edge devices and not for data centers.
C) It provides GPU optimization to handle high-throughput inference workloads.
D) It automatically tunes hyperparameters for all models.

2. You are designing a data pipeline for a healthcare analytics company that processes petabytes of structured and semi-structured patient data. Your goal is to optimize performance while maintaining accuracy.
Which of the following strategies is the most appropriate choice for accelerating this workload?

A) Use RAPIDS cuDF and Dask-cuDF for distributed preprocessing and RAPIDS cuML for GPU- accelerated machine learning model training.
B) Use RAPIDS cuDF for data ingestion, preprocessing, and transformation, then offload model training to scikit-learn on CPUs.
C) Load the data into a PostgreSQL database and run SQL queries for all transformations before training a model.
D) Use Apache Hadoop and MapReduce for preprocessing before training models on a single GPU.

3. A data scientist is training a deep learning model on an NVIDIA GPU-accelerated platform. The model is suffering from overfitting, leading to poor generalization on unseen data.
Which of the following techniques is the most effective for reducing overfitting in this scenario?

A) Reducing the learning rate
B) Removing data augmentation techniques
C) Applying dropout regularization
D) Increasing the number of layers in the model

4. Which of the following is the most efficient method for processing big data in a distributed environment using NVIDIA technologies?

A) Relying on traditional CPU-only processing frameworks for big data tasks.
B) Using cloud-based, non-GPU infrastructure for data processing
C) Utilizing a distributed data processing framework like Apache Spark with GPU acceleration from the NVIDIA RAPIDS library.
D) Using a single-node GPU-based solution for data processing.

5. A financial analyst is working with an irregularly spaced time-series dataset containing cryptocurrency transactions. The timestamps are not evenly distributed, with some periods having dense data and others having sparse entries. The analyst wants to visualize the data efficiently using GPU acceleration.
What is the best preprocessing approach before visualization?

A) Convert the dataset to Pandas and use df.resample() to aggregate by fixed time intervals.
B) Resample the time-series to a fixed frequency using cuDF.resample() and fill missing values.
C) Sort the data by timestamp and drop all sparse regions using df.dropna().
D) Ignore the irregularity and plot the raw timestamps directly without any preprocessing.

Solutions: