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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which of the following techniques are best suited for efficiently processing and organizing large datasets using NVIDIA technologies? (Select two)

A) Leveraging Dask for distributed GPU-based parallel computing
B) Using cuDF for GPU-accelerated DataFrame operations
C) Storing data directly on an HDD and processing with GPUs
D) Using TensorFlow with custom data pipelines for data loading

2. A data engineer is designing an Extract, Transform, Load (ETL) pipeline for a retail analytics platform that processes millions of customer transactions per day. The primary objective is to accelerate data ingestion, transformation, and storage while ensuring efficient scalability.
Which of the following approaches would be the most effective for optimizing this ETL workflow using NVIDIA-accelerated ETL tools?

A) Use Apache Spark with CPU-based processing instead of leveraging GPU acceleration.
B) Perform all transformations using Pandas DataFrames and then use multiprocessing to parallelize the workload on CPUs.
C) Use NVIDIA RAPIDS cuDF for data transformations and Dask-cuDF for parallelized processing across multiple GPUs.
D) Implement ETL processes using only SQL-based transformations within a relational database system.

3. 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) Dask
D) pandas

4. You have trained a machine learning model using cuML as part of the Modeling phase in the CRISP- DM framework. Now, you need to assess how well the model performs before moving forward with deployment.
Which of the following steps aligns best with the Evaluation phase of CRISP-DM using NVIDIA technologies?

A) Define the problem statement and collect relevant datasets before training the model.
B) Optimize the data pipeline using cudf.DataFrame.merge() to improve data loading speed.
C) Compute model accuracy, precision, and recall using cuml.metrics.accuracy_score() and cuml.metrics.classification_report().
D) Deploy the model to an edge device using TensorRT for real-time inference.

5. A data scientist is working with large-scale datasets in a RAPIDS AI pipeline and needs to efficiently process and organize the data while leveraging GPU acceleration.
Which of the following approaches best ensures optimized processing and memory management when using NVIDIA technologies?

A) Use cuDF to store and process data in GPU memory, leveraging its vectorized operations for transformations and aggregations.
B) Write intermediate data to disk as CSV files before reading them back into RAPIDS AI to avoid memory overflow.
C) Store data in a pandas DataFrame first and then convert it to cuDF only when GPU operations are needed.
D) Process data using Apache Spark on CPU before transferring results to GPU for model training.

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