Free NVIDIA NCP-ADS Exam Dumps Questions & Answers

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.  Use pandas to load the dataset and perform the operations using standard pandas functions
• C.  Load the dataset using cuDF, then use cuDF's built-in .dropna(), .drop_duplicates(), and .astype() methods
• D.  Convert the dataset into a NumPy array and process it using CuPy before converting it back to a DataFrame

You are tasked with implementing data caching to reduce shuffle in an accelerated machine learning pipeline using NVIDIA technologies. You need to cache intermediate results after a shuffle operation in a distributed setting.
Which of the following is the best approach to minimize shuffle overhead and maximize performance?

• A.  Cache the data in GPU memory using RAPIDS cuDF for faster access, and leverage GPU-based partitioning to reduce shuffle size.
• B.  Use DALI to perform preprocessing and cache the output before the shuffle operation, thereby eliminating the need for shuffle.
• C.  Use RAPIDS cuDF to cache the shuffled data on disk and then re-load it from disk during subsequent stages.
• D.  Implement Spark's default disk caching to store shuffle results, allowing the GPU to access disk data directly.

You are working on an AI-driven customer behavior prediction project.
According to the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, what is the most critical task to complete during the Data Understanding phase?

• A.  Selecting the most appropriate machine learning algorithm for the prediction task.
• B.  Identifying and preparing feature engineering strategies to improve model accuracy.
• C.  Acquiring and exploring the dataset to assess quality, completeness, and potential biases.
• D.  Deploying the model into a production environment for real-time inference.

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

A data science team wants to leverage GPU acceleration for detecting anomalies in a massive IoT sensor dataset that is continuously streaming.
Which of the following NVIDIA-supported methods would be the best for handling real-time anomaly detection in a high-throughput environment?

• A.  Implement a GPU-accelerated spectral residual anomaly detection model using NVIDIA Merlin for rapid feature selection.
• B.  Use RAPIDS cuML's GPU-accelerated DBSCAN to cluster the sensor readings and classify outliers as anomalies.
• C.  Utilize NVIDIA Morpheus with deep learning-based anomaly detection to process high-throughput log data efficiently.
• D.  Deploy a TensorRT-optimized Transformer model to process the time-series data in parallel for real- time anomaly detection.