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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

2. A data science team wants to deploy a GPU-accelerated pipeline using cuGraph to analyze graph data on cloud infrastructure. They are evaluating different cloud-based GPU solutions.
Which of the following factors should they consider when selecting a cloud-based GPU instance for running cuGraph efficiently?

A) The choice of GPU instance does not affect cuGraph performance since all GPUs execute graph algorithms at the same speed.
B) cuGraph runs equally well on CPU-based virtual machines, making GPU instances unnecessary.
C) Cloud-based GPUs are only useful for rendering graphics, not for running cuGraph algorithms.
D) The availability of NVIDIA CUDA-enabled GPUs, as cuGraph requires CUDA for acceleration.

3. You are building a fraud detection system and have a dataset that includes a feature representing transaction amounts. The values range from a few cents to several thousand dollars.
What is the most appropriate data type for this feature?

A) Floating-point (with precision to store cents or fractions of a cent).
B) Boolean (True for transactions above $100, False otherwise).
C) Integer (rounding all transaction amounts to whole dollars).
D) Categorical (grouping transactions into bins such as "low", "medium", "high").

4. After profiling a deep learning model using NVIDIA DLProf, you notice that a specific GEMM (General Matrix Multiplication) operation takes significantly longer than expected. The profiler output reveals that tensor cores are underutilized despite having an Ampere-based GPU with Tensor Cores enabled.
Which of the following actions is the MOST appropriate to improve performance?

A) Convert the model's data type to float16 or bfloat16 and re-run the training with automatic mixed precision (AMP).
B) Disable CUDA graphs and enforce PyTorch's eager execution mode to improve kernel execution order.
C) Increase the batch size to maximize GPU memory usage and reduce kernel launch overhead.
D) Switch from stochastic gradient descent (SGD) to Adam optimizer, as Adam improves convergence and computational efficiency.

5. A data scientist is working with a dataset of sensor readings (temperature, pressure, vibration) in different scales and units. To ensure all features contribute equally to a machine learning model, the data needs to be standardized.
Which approach is best for standardizing numerical features?

A) Apply z-score normalization (standardization) to scale values based on mean and standard deviation.
B) Convert all numerical features to categorical values using binning.
C) Apply log transformation to all numerical columns to force them into a uniform distribution.
D) Use Min-Max scaling to transform values into a fixed range (e.g., [0,1] or [-1,1]).

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