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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are consulting for a retail company that collects data from daily sales transactions, customer interactions, and inventory tracking across multiple locations. They are unsure whether their dataset qualifies as big data and which processing method would be most suitable.
Which of the following characteristics best indicate that the dataset requires big data processing and acceleration techniques?

A) The dataset includes a mix of numerical and categorical variables, requiring additional preprocessing
B) The dataset contains more than 1 million records, making it impossible to process using pandas
C) The dataset is stored in multiple relational database tables, making querying inefficient
D) The dataset exceeds the memory capacity of a single machine and requires distributed or GPU- accelerated processing

2. Which of the following actions can you perform using DLProf to analyze a deep learning model's performance?

A) Increase batch size to improve accuracy
B) Visualize GPU memory utilization over time
C) Modify the training dataset during model execution
D) Automatically adjust the learning rate based on the model's convergence

3. You are training a machine learning model using NVIDIA RAPIDS cuML and notice that the training process is significantly slower than expected. You suspect that there are bottlenecks in data movement and computation.
Which of the following techniques can best help you diagnose and resolve these bottlenecks?

A) Use cuml.common.device_auto_mem_size() to check GPU memory usage and adjust batch sizes accordingly.
B) Use cudf.DataFrame.to_pandas() to convert the dataset to a pandas DataFrame for analysis.
C) Reduce the number of features used in training without profiling the actual bottlenecks.
D) Move all data from GPU memory to CPU memory before training the model.

4. A company is deploying an MLOps pipeline for training and serving deep learning models. The data scientists want to leverage GPU acceleration at multiple stages of the pipeline to enhance efficiency.
Which of the following steps would benefit the most from GPU acceleration?

A) Storing and retrieving models from a centralized object storage system.
B) Model monitoring by logging metadata and performance metrics in a database.
C) Training and inference workloads using deep learning models with TensorFlow or PyTorch.
D) Running CI/CD workflows for code integration and deployment using a traditional CPU-based Jenkins setup.

5. A machine learning engineer wants to evaluate the performance of NVIDIA RAPIDS cuDF and Apache Spark for large-scale data processing on a GPU-enabled cluster.
Which of the following strategies is the most effective for obtaining a fair and comprehensive benchmark?

A) Run Spark on a CPU cluster while running RAPIDS on a GPU to compare real-world scenarios.
B) Limit the benchmark to small datasets since GPUs excel at parallel processing.
C) Execute identical ETL workflows on cuDF and Spark-RAPIDS and measure execution time and resource utilization.
D) Focus only on processing speed without considering resource consumption differences between frameworks.

Solutions: