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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You have a pandas DataFrame with a column containing floating-point numbers, but it takes up too much memory. You want to convert it into a lower-precision type using CuDF or pandas while ensuring computational efficiency.
Which function would you use?

A) df.astype('float16')
B) df.convert_dtypes()
C) df.to_float16()
D) df['col'].apply(lambda x: np.float16(x))

2. A machine learning team is handling large-scale datasets that need to be efficiently stored and accessed within an NVIDIA RAPIDS workflow.
Which of the following storage formats and techniques provides the best performance for GPU-based data science pipelines?

A) Use CSV files for storage, as they are widely compatible and can be read quickly using cuDF's read_csv() method.
B) Store data in SQLite databases and query it into pandas before converting it to cuDF.
C) Save data in JSON format to ensure hierarchical relationships and flexibility before loading it into cuDF.
D) Store data in the Apache Parquet format and load it directly into cuDF using cuDF's read_parquet() method.

3. A data scientist is analyzing sales data for an e-commerce company that experiences strong seasonal trends (e.g., increased sales during holiday seasons). The goal is to accurately forecast future sales using GPU-accelerated data science techniques.
Which approach would be the most effective?

A) Compute a rolling average and manually adjust for seasonality based on previous peak sales months.
B) Train a Decision Tree model using cuML to classify future sales trends.
C) Use a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and optimize it using NVIDIA RAPIDS.
D) Apply k-Means clustering to identify seasonal patterns and extrapolate future values.

4. You are working on a large-scale machine learning project that requires preprocessing terabytes of structured and semi-structured data. You need a distributed data processing framework that can leverage NVIDIA GPUs efficiently to accelerate computations.
Which of the following approaches would best achieve this goal?

A) Using Dask with RAPIDS cuDF and cuML for distributed GPU-accelerated processing
B) Using Apache Spark with PySpark for CPU-based distributed data processing
C) Using plain NumPy with CUDA extensions to manually parallelize computations across multiple GPUs
D) Using TensorFlow's Dataset API to load and preprocess the data on GPUs

5. You are working with a GPU-based cloud environment and need to optimize the memory usage for a dataset that contains a column item_id representing unique product IDs. The item_id values are large integers, and there are over 10 million distinct product IDs.
Which of the following is the most memory-efficient data type choice for this column?

A) df['item_id'] = df['item_id'].astype('string')
B) df['item_id'] = df['item_id'].astype('float64')
C) df['item_id'] = df['item_id'].astype('int64')
D) df['item_id'] = df['item_id'].astype('int32')

Solutions: