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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a large-scale data processing pipeline that involves multi-GPU acceleration using Dask. The dataset is too large to fit into the memory of a single GPU, so you decide to distribute the workload across multiple GPUs using Dask-CUDA.
Which of the following steps is necessary to implement efficient data parallelism across multiple GPUs in a Dask-based workflow?

A) Convert Dask DataFrames into Pandas DataFrames to leverage GPU acceleration through Pandas' built-in multi-threading support.
B) Explicitly initialize a LocalCUDACluster with multiple workers, ensuring each worker is assigned a single GPU.
C) Use the DaskExecutor from RAPIDS cuML to offload data processing tasks to distributed GPU workers.
D) Use dask.dataframe.read_parquet() to load the dataset and let Dask automatically distribute computations across multiple GPUs.

2. A data scientist is training a deep learning model on an NVIDIA GPU-accelerated platform. The model is suffering from overfitting, leading to poor generalization on unseen data.
Which of the following techniques is the most effective for reducing overfitting in this scenario?

A) Removing data augmentation techniques
B) Increasing the number of layers in the model
C) Applying dropout regularization
D) Reducing the learning rate

3. You are tasked with comparing the performance of different GPU-accelerated frameworks for a deep learning model. The frameworks you are considering are TensorFlow, PyTorch, and CUDA. To evaluate the performance, you decide to implement a benchmark that measures GPU efficiency, memory usage, and speed.
Which of the following actions should you take to design an effective benchmark? (Select two)

A) Use CPU-based implementations of the same frameworks for comparison.
B) Measure GPU utilization and memory usage, but ignore the network and disk I/O.
C) Use a batch size that is optimal for each framework's memory management.
D) Use a common dataset for all frameworks to ensure comparability.
E) Benchmark only the training phase of the deep learning model.

4. You are designing an ETL workflow to process large-scale financial transaction data using GPU acceleration. The dataset is stored in a Parquet file and contains millions of records.
Which of the following approaches is the most efficient for performing extract, transform, and load (ETL) operations using NVIDIA RAPIDS technologies?

A) Use Pandas DataFrame for transformation, and then convert the dataset to cuDF before writing to storage.
B) Load the Parquet file directly into a cuDF DataFrame and use cuDF's built-in functions for transformations before writing the results back to storage.
C) Use Apache Spark with CPU-based processing for ETL, then convert the results into cuDF for accelerated analytics.
D) Store all data as CSV files and perform ETL operations using traditional row-based processing.

5. A data scientist is preprocessing a dataset containing several types of features:
A timestamp column storing millisecond-resolution timestamps.
A column with binary categorical values (Yes/No).
A column containing large continuous numerical values.
A column containing product category codes ranging from 0 to 5000.
Which of the following data type choices is the most optimal for maximizing GPU processing efficiency using NVIDIA cuDF?

A) Use string data type for timestamps, int32 for binary values, float16 for continuous numerical values, and int64 for product category codes.
B) Binary categorical values (Yes/No) should be stored as bool, which takes up minimal space.
C) Use float64 for timestamps, int8 for binary categorical values, float32 for continuous numerical values, and int32 for product category codes.
D) float32 is the best choice for large continuous numerical values, balancing precision and GPU efficiency.
E) Store timestamps as int64, encode binary values as float16, use float64 for continuous numerical values, and use int8 for product category codes.
F) Convert timestamps to datetime64[ms], encode binary values as bool, use float32 for continuous values, and int16 for product category codes.
G) Product category codes (range: 0-5000) fit within int16 (which can hold values from -32,768 to
32,767), making it more memory-efficient than int32.

Solutions: