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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are analyzing a dataset containing millions of transaction records to detect fraudulent transactions. The dataset is highly imbalanced, with fraudulent cases making up only 0.5% of the data.
Which data analysis strategy will most effectively handle the imbalance while minimizing the risk of overfitting?

A) Random undersampling of the majority class
B) Stratified random sampling
C) SMOTE (Synthetic Minority Over-sampling Technique)
D) Random oversampling of the minority class

2. You are tasked with implementing a multi-GPU data pipeline using Dask-CUDA to process large datasets stored in Parquet format. Your goal is to achieve optimal GPU memory utilization and minimize inter-GPU communication overhead.
Which of the following approaches best aligns with these goals?

A) Use dask.persist() instead of dask.compute() to force immediate execution of tasks before distribution to GPUs.
B) Set dask.config.set({'distributed.worker.memory.target': 0.9}) to allocate 90% of the total CPU memory for GPU operations.
C) Use dask.array instead of dask_cudf because it provides better performance for structured tabular data.
D) Use dask_cudf.read_parquet() with split_row_groups=True to evenly distribute data across GPUs.

3. A machine learning engineer is training a large transformer-based model for natural language processing (NLP). They want to maximize training speed and efficiency using NVIDIA GPUs.
Which of the following techniques would most effectively enhance GPU utilization and reduce training time?

A) Disabling data parallelism
B) Using mixed-precision training with Tensor Cores
C) Running training exclusively on CPU
D) Prefetching data with the CPU while training on the GPU

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

5. A data scientist is working with a 50 TB dataset consisting of structured logs from IoT devices. The data needs to be cleaned, transformed, and aggregated before training a machine learning model.
Which of the following frameworks would be the most efficient choice for distributed data processing?

A) Apache Spark with RAPIDS Accelerator
B) SQLite
C) Pandas
D) Python multiprocessing module

Solutions: