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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

A machine learning engineer is tasked with deploying a real-time image classification model as part of an MLOps pipeline. The model requires low-latency inference and must handle high-throughput requests efficiently.
Which of the following deployment strategies is the most suitable for leveraging GPU acceleration?

A. Using a REST API wrapper to load the model dynamically into memory for each request.
B. Running the model inference on a multi-core CPU server with batch processing enabled.
C. Using an Apache Spark cluster with distributed CPU-based inference.
D. Deploying the model using NVIDIA Triton Inference Server with TensorRT optimizations.

Question 2

You are working on a machine learning project that requires training a large XGBoost model on a dataset containing millions of records. Due to the dataset size, training on a CPU-based environment takes an excessively long time. To accelerate the training process, you decide to use NVIDIA RAPIDS.
Which of the following is the best approach to leverage GPU acceleration for training the XGBoost model?

A. Install and use dask-xgboost, which automatically optimizes XGBoost training using GPU acceleration.
B. Use XGBoost with the "tree_method": "gpu_hist" parameter to enable GPU acceleration.
C. Store the dataset in Apache Parquet format and load it using pandas to improve training performance.
D. Use cuML to replace scikit-learn's XGBoost implementation, as cuML supports GPU-accelerated XGBoost training.

Question 3

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

Question 4

A research team is analyzing large-scale social interactions and wants to identify strongly connected communities within a massive graph dataset using NVIDIA's cuGraph library.
Which method would be the most efficient for this task?

A. Use cuGraph's Louvain method to detect hierarchical communities based on modularity optimization.
B. Apply cuGraph's Dijkstra's algorithm to find the shortest paths between all nodes and group them into communities.
C. Run the cuGraph PageRank algorithm and classify nodes with high scores as community leaders.
D. Apply cuGraph's Label Propagation Algorithm (LPA) to divide the graph into communities without predefining the number of clusters.

Question 5

A financial institution is developing an ETL pipeline to ingest and process large volumes of streaming data from various sources, including stock market feeds, real-time transactions, and economic indicators. The ETL process must be highly efficient to minimize latency while ensuring data integrity.
Which of the following strategies is best suited for implementing a high-performance, GPU-accelerated ETL pipeline?

A. Load data directly into an Excel spreadsheet and use VBA macros to clean and transform it.
B. Utilize NVIDIA Morpheus with RAPIDS to preprocess real-time streaming data using GPU acceleration.
C. Store all streaming data in a PostgreSQL database before performing batch transformations.
D. Use Pandas and Python's built-in threading library to handle concurrent data ingestion and transformation.

Solutions: