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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are tasked with processing a large dataset using multiple GPUs to accelerate computation. You decide to use Dask to implement data parallelism with NVIDIA's RAPIDS framework to maximize GPU utilization.
Which of the following steps is essential for efficiently distributing the workload across multiple GPUs in Dask?

A) Manually allocate GPU memory using cupy for each worker instead of using Dask's scheduler.
B) Set up a single Dask dataframe without partitioning and rely on automatic workload balancing.
C) Use dask.dataframe.repartition() to distribute data evenly across multiple GPUs.
D) Use dask_cuda.LocalCUDACluster() to create a multi-GPU cluster and dask.distributed.Client() to manage execution.

2. You are working on a dataset containing missing values, duplicate records, and inconsistent data types.
The dataset size is 15GB and you need to efficiently perform data cleansing operations such as:
- Handling missing values
- Dropping duplicates
- Converting data types
Which of the following approaches would be the most efficient way to perform these operations on an NVIDIA GPU?

A) Use Vaex to perform data cleansing, as it is optimized for large-scale datasets
B) Convert the dataset into a NumPy array and process it using CuPy before converting it back to a DataFrame
C) Use pandas to load the dataset and perform the operations using standard pandas functions
D) Load the dataset using cuDF, then use cuDF's built-in .dropna(), .drop_duplicates(), and .astype() methods

3. Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

A) Z-score Standardization
B) Robust Scaling
C) Min-Max Scaling
D) Log Transformation

4. You are tasked with profiling a PyTorch-based deep learning model to identify performance bottlenecks using NVIDIA DLProf. Your goal is to analyze kernel execution times and identify operations causing excessive memory consumption.
Which of the following steps is the MOST appropriate sequence for profiling using DLProf?

A) Profile the model using torch.profiler, then compare the results against the DLProf report to analyze GPU-specific kernel optimizations.
B) Use nvidia-smi to capture GPU utilization metrics, then manually correlate high utilization periods with the training script to determine bottlenecks.
C) Run dlprof --mode=default --output_path=profile_results on the training script, analyze the generated report, and optimize memory-intensive operations.
D) Execute the training script under DLProf TensorBoard mode to visualize performance insights, then re-run the model with automatic mixed precision (AMP) to reduce memory usage.

5. A retail company is deploying an AI-driven demand forecasting system using NVIDIA GPUs. The team follows the CRISP-DM framework and is currently in the Evaluation phase.
Which approach best leverages NVIDIA technologies to assess model performance effectively?

A) Use RAPIDS cuML to rapidly compute evaluation metrics like RMSE and R-squared on large datasets using GPUs.
B) Assume that a high training accuracy guarantees excellent real-world performance, skipping the evaluation phase.
C) Perform evaluation on a small CPU-based subset of the dataset instead of using full GPU-accelerated inference.
D) Rely only on training loss as the primary evaluation metric without considering validation performance.

Solutions: