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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. When scaling data parallelism using Dask with multiple Nvidia GPUs, what is the key consideration to avoid memory issues when distributing large datasets?

A) Split the dataset into smaller partitions that fit into each GPU's memory to prevent out-of-memory errors, and let Dask manage data distribution.
B) Ensure that each GPU's memory usage is manually monitored and adjusted, as Dask does not manage memory allocation automatically across GPUs.
C) Use dask_gpu instead of dask_cuda to manage memory automatically across GPUs.
D) Allow Dask to allocate data chunks dynamically without partitioning the dataset first, letting the system handle memory distribution automatically.

2. Which of the following is the most efficient way to implement data parallelism using Dask for multi- GPU scaling on an Nvidia platform?

A) Use Dask with the dask_cuda package to distribute computation across GPUs, ensuring that each GPU is responsible for a portion of the data, while using distributed.Client to connect the GPU workers.
B) Use Dask with a single GPU, distributing data across multiple workers within the GPU without considering GPU memory limitations.
C) Use Dask on a single GPU machine with no GPU-specific optimization, treating the system as if it were CPU-only.
D) Use Dask with the dask_gpu package to assign data chunks across GPUs manually, without utilizing the distributed.Client.

3. You are processing large-scale datasets in Dask-cuDF and observe that your computation involves excessive data shuffling, which slows down performance. You decide to implement data caching to reduce shuffle overhead.
Which of the following best describes a technique to reduce shuffle costs in a Dask-cuDF workflow?

A) Using .compute() on each partition immediately forces Dask to materialize results and store them in memory, ensuring shuffle operations are avoided.
B) Splitting the DataFrame into multiple smaller DataFrames and recomputing each separately reduces shuffle operations in Dask-cuDF.
C) Persisting intermediate results in distributed GPU memory using df.persist() ensures that Dask does not recompute partitions, reducing shuffle overhead.
D) Disabling lazy execution using dask.config.set(lazy=False) forces all computations to execute eagerly, thereby avoiding shuffle.

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

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

Solutions: