NVIDIA-Certified-Professional Accelerated Data Science NCP-ADS Prüfungsfragen mit Lösungen:

1. You are tasked with profiling a PyTorch-based deep learning model to identify performance bottlenecks using NVIDIA DLProf. Your goal is to analyze kernel execution times and identify operations causing excessive memory consumption.
Which of the following steps is the MOST appropriate sequence for profiling using DLProf?

A) Run dlprof --mode=default --output_path=profile_results on the training script, analyze the generated report, and optimize memory-intensive operations.
B) Execute the training script under DLProf TensorBoard mode to visualize performance insights, then re-run the model with automatic mixed precision (AMP) to reduce memory usage.
C) Profile the model using torch.profiler, then compare the results against the DLProf report to analyze GPU-specific kernel optimizations.
D) Use nvidia-smi to capture GPU utilization metrics, then manually correlate high utilization periods with the training script to determine bottlenecks.

2. You are working with a large time-series dataset consisting of millions of records and want to efficiently visualize trends over time using NVIDIA technologies. The dataset is stored as a cuDF DataFrame, and you need to generate an interactive line plot with minimal performance overhead.
Which of the following is the best approach to achieve this goal?

A) Use the hvPlot library with RAPIDS cuDF to directly render the time-series data interactively
B) Convert the cuDF DataFrame to a Pandas DataFrame and plot using Matplotlib
C) Load the data into a Spark DataFrame and visualize using Apache Zeppelin
D) Use the Bokeh library to plot the time-series data from a cuDF DataFrame directly

3. A data scientist is working with a large dataset for a machine learning model and wants to accelerate feature engineering using a GPU.
Which of the following approaches will provide the most significant performance boost when using GPU acceleration?

A) Using RAPIDS cuDF and cuML libraries to perform feature transformations on a GPU.
B) Reducing dataset size by randomly removing data points without considering class balance.
C) Using traditional pandas DataFrames and NumPy operations optimized for CPU processing.
D) Using a single-threaded feature extraction approach to avoid overhead from parallelization.

4. You are working on a financial fraud detection system using NVIDIA RAPIDS cuML. You have a large time-series dataset of transaction amounts over time, and you need to identify anomalies such as unusual spikes in transactions.
Which of the following approaches is the most appropriate for detecting anomalies using NVIDIA technologies?

A) Use cuML's DBSCAN clustering to identify anomalies based on density differences
B) Train a deep learning model with TensorFlow and deploy it using NVIDIA Triton Inference Server for anomaly detection
C) Apply cuML's PCA-based anomaly detection to detect outliers in the principal component space
D) Run standard Pandas-based anomaly detection methods on CPU for better precision

5. You are tasked with processing a large dataset of 100 million records for a deep learning project using NVIDIA technologies. You need to determine the most efficient data processing library for this task to maximize performance and reduce processing time.
Which of the following libraries is best suited for this task?

A) cuDF
B) PySpark
C) pandas
D) Dask

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