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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on optimizing a deep learning model for inference on an NVIDIA GPU. You decide to use NVIDIA DLProf to profile the model and analyze its performance. After running DLProf, you review the generated reports and find that the GPU Utilization is significantly lower than expected.
Which of the following is the most likely reason for this issue, as indicated by the profiling data?

A) DLProf detected a high level of tensor core utilization, which generally indicates poor performance.
B) The batch size is too large, leading to excessive memory allocation failures.
C) The GPU lacks sufficient VRAM, causing frequent memory swaps to system RAM.
D) The model contains a large number of small, inefficient kernel launches that introduce overhead.

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

3. You are developing an accelerated ETL workflow that requires data transformations such as filtering, aggregating, and joining large datasets. You decide to leverage NVIDIA GPUs to accelerate the transformation phase of your ETL pipeline.
Which of the following approaches will provide the greatest performance improvements when working with large-scale tabular datasets?

A) Using TensorFlow for data transformation tasks
B) Relying on traditional pandas for in-memory transformations
C) Using RAPIDS cuDF to perform transformations on a GPU
D) Performing transformations using SQL-based queries on CPU

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

5. A machine learning engineer is working with a financial dataset that contains multiple numerical features, including income, loan amount, and transaction frequency. Some features are normally distributed, while others have a highly skewed distribution with extreme outliers.
Which of the following approaches best ensures uniformity across features before training a model?

A) Scale all numerical features using min-max normalization
B) Use one-hot encoding to transform numerical features into categorical representations
C) Remove outliers before applying standardization
D) Apply log transformation to skewed features before standardizing them with z-score normalization

Solutions: