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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

Which of the following tools or techniques are essential for effectively working with large-scale data in a distributed environment? (Select two)

A. Using Apache Spark for distributed data processing
B. Using SQLAlchemy to interact with databases for large data processing
C. Using SQLite as a local database for large-scale data analysis
D. Using Excel to manipulate large datasets
E. Using Dask for parallel processing of large datasets

Question 2

You are tasked with optimizing the performance of an MLOps pipeline that uses GPU-accelerated workflows. After running initial benchmarks, you notice that the training time is higher than expected, despite the use of multiple GPUs.
What are the best strategies to optimize the GPU-accelerated workflow in this case? (Select two)

A. Disable gradient accumulation when using multi-GPU setups to increase communication efficiency.
B. Ensure efficient multi-GPU communication and synchronization strategies, such as using NCCL for distributed training.
C. Increase the batch size to better utilize the multiple GPUs and reduce the number of updates to the model during training.
D. Reduce the number of GPUs used and focus on fine-tuning the hyperparameters for optimal performance on a single GPU.
E. Ensure that the model is distributed evenly across GPUs to prevent some GPUs from being underutilized.

Question 3

You are working with a large dataset containing customer transactions and want to perform exploratory data analysis (EDA) efficiently. Given the dataset's size, you decide to use NVIDIA RAPIDS to accelerate the process.
Which of the following approaches is the most effective for conducting EDA using NVIDIA technologies?

A. Load the dataset into Pandas and use Matplotlib for visualization
B. Use TensorFlow and Keras to preprocess the data before performing EDA
C. Use RAPIDS cuDF to perform fast dataframe operations and visualize results with cuXfilter
D. Perform SQL queries on a CPU-based database for initial data analysis before GPU acceleration

Question 4

You are working on an MLOps pipeline that involves loading a large dataset for training a deep learning model on an NVIDIA GPU. Before training, you need to ensure that the dataset fits within the available GPU memory.
Which of the following commands in Python using the pandas and numpy libraries can correctly determine the memory size of a dataset?

A. df.info(memory_usage='deep')
B. df.memory_usage(deep=True).sum()
C. np.array(df).nbytes
D. sys.getsizeof(df)

Question 5

You are working with a large dataset in RAPIDS cuDF and plan to standardize the numerical features using cuml.preprocessing.StandardScaler(). However, some columns contain missing values.
What is the best approach to handle the missing values before applying standardization?

A. Replace missing values with zero before standardization.
B. Use cudf.DataFrame.fillna(method='ffill') to forward-fill missing values.
C. cuml.impute.KNNImputer() to replace missing values based on k-nearest neighbors.
D. Use cuml.impute.SimpleImputer(strategy='mean') to replace missing values with the column mean.

Solutions: