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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a deep learning project that requires a large dataset of high-resolution satellite images for training a convolutional neural network (CNN). You want to leverage NVIDIA technologies to efficiently acquire and manage the dataset.
Which of the following approaches is the most suitable?

A) Use NVIDIA DALI (Data Loading Library) to stream and preprocess satellite image data efficiently for deep learning training.
B) Use NVIDIA Modulus to generate synthetic satellite images instead of acquiring real-world data.
C) Use NVIDIA DeepStream to acquire satellite images and store them in a structured dataset for machine learning.
D) Use NVIDIA RAPIDS cuDF to directly download and preprocess satellite images from an API in real time.

2. 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

3. You are a data scientist working on a large-scale deep learning project that requires significant computational resources. You have the option to run your workloads on a cloud-based GPU instance.
Which of the following statements best describes a key benefit of using cloud-based GPUs for your workload?

A) Cloud-based GPUs provide consistent and predictable performance, identical to on-premise dedicated GPUs.
B) Cloud-based GPUs enable scalable resource allocation, allowing you to dynamically increase or decrease GPU instances as needed.
C) Cloud-based GPUs eliminate all data transfer bottlenecks and latencies when training models on large datasets.
D) Cloud-based GPUs are always more cost-effective than on-premise GPUs, regardless of workload size and duration.

4. You are working with a large dataset using NVIDIA RAPIDS cuDF and need to normalize a numerical column (price) to scale its values between 0 and 1.
Which of the following approaches correctly normalizes the column using cuDF?

A) df["price"] = ( 2. df["price"] - df["price"].min() 3. ) / (df["price"].max() - df["price"].min())
B) df["price"] = (df["price"] - df["price"].mean()) / df["price"].std()
C) df["price"] = df["price"].applymap( 2. lambda x: (x - df["price"].min()) 3. / (df["price"].max() - df["price"].min()) 4. )
D) df["price"] = df["price"] / df["price"].max()

5. A financial analyst wants to create an interactive GPU-accelerated dashboard to visualize stock price movements in real-time.
Which NVIDIA-supported tool is best suited for this purpose?

A) Use Plotly Dash with RAPIDS cuDF to create an interactive GPU-powered dashboard.
B) Convert the stock price dataset into a NumPy array and visualize it using Seaborn's line plot.
C) Rely on Matplotlib to generate static plots and update them every minute with a loop.
D) Precompute the time-series visualization with Dask and display it in a static HTML page.

Solutions: