Online Access Free NCP-ADS Exam Questions

You are working with a dataset where numerical features have different scales. To ensure uniformity across features, you decide to standardize the data using NVIDIA RAPIDS cuML.
Which of the following methods correctly standardizes the data in a GPU-accelerated manner?

• A.1. scaler = cuml.preprocessing.StandardScaler() 2. df = scaler.fit_transform(df)
• B.df = df.apply(lambda x: (x - x.mean()) / x.std(), axis=1)
• C.df = (df - df.mean()) / df.std()
• D.df = (df - df.min()) / (df.max() - df.min())

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

A data scientist is deploying a deep learning model to production using an NVIDIA GPU-powered inference server. They want to ensure low latency and high throughput while maintaining model accuracy.
Which of the following deployment strategies would be most effective?

• A.Disabling mixed-precision inference to avoid accuracy loss
• B.Running inference on a CPU instead of a GPU to reduce power consumption
• C.Deploying a model without batching to ensure real-time responses
• D.Using NVIDIA Triton Inference Server with model ensemble optimization

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.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.
• C.Run dlprof --mode=default --output_path=profile_results on the training script, analyze the generated report, and optimize memory-intensive operations.
• D.Use nvidia-smi to capture GPU utilization metrics, then manually correlate high utilization periods with the training script to determine bottlenecks.

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