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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are running a data science project on a cloud environment, where you need to optimize the GPU utilization for real-time data processing tasks.
Which of the following practices should you consider to maximize GPU performance? (Select two)

A) Implement model inference in a batch mode rather than in a real-time streaming mode for better performance.
B) Use NVIDIA CUDA libraries optimized for data science tasks to accelerate computations.
C) Use data parallelism techniques to split the workload evenly across multiple GPUs.
D) Prioritize using CPU for computationally expensive tasks and reserve GPU for I/O operations.
E) Scale the cloud infrastructure vertically by increasing the size of the GPU in use.

2. You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.
Which of the following frameworks would be the best choice for handling this workload?

A) Apache Spark with its default CPU-based execution
B) Pandas with multiprocessing
C) RAPIDS + Dask for distributed GPU-accelerated ETL
D) Hadoop MapReduce

3. You are managing a data processing pipeline that utilizes NVIDIA RAPIDS on GPUs for accelerated data transformations. During execution, you notice that the pipeline is not achieving expected performance gains.
What is the most effective approach to monitor and diagnose bottlenecks in this pipeline using NVIDIA technologies?

A) Reduce the dataset size and rerun the pipeline without profiling tools to check for performance improvements.
B) Enable RAPIDS memory pool logging to check for memory fragmentation and out-of-memory errors.
C) Run the pipeline on CPU instead of GPU to compare execution times.
D) Use NVIDIA Nsight Systems to profile kernel execution times and memory transfers.

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

5. You are building a fraud detection system and have a dataset that includes a feature representing transaction amounts. The values range from a few cents to several thousand dollars.
What is the most appropriate data type for this feature?

A) Boolean (True for transactions above $100, False otherwise).
B) Floating-point (with precision to store cents or fractions of a cent).
C) Categorical (grouping transactions into bins such as "low", "medium", "high").
D) Integer (rounding all transaction amounts to whole dollars).

Solutions: