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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

2. You are working with a dataset consisting of 100 million records stored in a distributed system. The dataset includes numerical and categorical variables, requiring both exploratory data analysis (EDA) and machine learning model training. The processing time using traditional CPU-based methods is too slow.
Which of the following techniques would be the most effective acceleration method to handle this workload efficiently?

A) Store the dataset in a relational database and query it using SQL
B) Use RAPIDS cuDF for GPU-accelerated data processing
C) Scale up to a high-core-count CPU machine
D) Reduce the dataset to a smaller sample size before processin

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

4. A data scientist is working with an imbalanced dataset in a fraud detection project. The dataset contains 1 million transactions, but only 2% of them are labeled as fraudulent. To improve the performance of the model, the scientist decides to generate synthetic data using NVIDIA RAPIDS cuDF.
Which of the following approaches is the best way to generate synthetic samples while preserving data characteristics?

A) Use cudf.DataFrame.append(cudf.DataFrame.random()) to create new fraudulent transactions.
B) Use cudf.DataFrame.sample(frac=0.5, replace=True) to oversample the minority class.
C) Apply cuML.SMOTE() to generate synthetic samples based on the minority class distribution.
D) Use cudf.DataFrame.interpolate(method='linear') to create new fraudulent samples by interpolating between existing ones.

5. 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 are always more cost-effective than on-premise GPUs, regardless of workload size and duration.
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 provide consistent and predictable performance, identical to on-premise dedicated GPUs.

Solutions: