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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with a large dataset containing millions of high-resolution images for a deep learning project. The dataset needs to be processed efficiently on a GPU before training a model.
Which NVIDIA technology is best suited for preprocessing, augmenting, and efficiently loading the dataset into memory?

A) NVIDIA Nsight Compute to optimize image dataset processing.
B) NVIDIA Triton Inference Server to preprocess the dataset before model training.
C) NVIDIA RAPIDS cuDF to transform image data into tabular format for analysis.
D) NVIDIA DALI (Data Loading Library) to accelerate data loading and preprocessing on the GPU.

2. You have trained a machine learning model and are deploying it into production in a cloud-based environment. You need to ensure the model is continuously monitored for performance, including drift in input data and prediction quality.
Which of the following techniques should you use to effectively monitor the model in production? (Select two)

A) Set up continuous model retraining pipelines triggered by data drift or performance degradation.
B) Restrict access to the model's API to minimize the number of calls and reduce monitoring overhead.
C) Use manual inspection of model predictions periodically to assess accuracy in production.
D) Implement model versioning and rollback procedures to maintain consistency in production models.

3. You are implementing a GPU-accelerated ETL pipeline that involves joining two large datasets:
Dataset A: A cuDF DataFrame with 10 million customer records.
Dataset B: A cuDF DataFrame with 100 million transaction records.
The goal is to efficiently perform a join operation to link customer details with transaction data, ensuring that the pipeline remains scalable and performant.
Which of the following is the best approach to optimize the join operation using NVIDIA RAPIDS?

A) Use cuDF's .merge() function and ensure both DataFrames have the correct index before joining
B) Store the transaction data as a CSV file and perform joins using SQL queries before loading it into cuDF
C) Convert both DataFrames to Pandas before performing the join for compatibility
D) Perform the join operation entirely in a CPU-based Spark environment for better stability

4. A data scientist is analyzing sales data for an e-commerce company that experiences strong seasonal trends (e.g., increased sales during holiday seasons). The goal is to accurately forecast future sales using GPU-accelerated data science techniques.
Which approach would be the most effective?

A) Train a Decision Tree model using cuML to classify future sales trends.
B) Apply k-Means clustering to identify seasonal patterns and extrapolate future values.
C) Compute a rolling average and manually adjust for seasonality based on previous peak sales months.
D) Use a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and optimize it using NVIDIA RAPIDS.

5. You are working with a dataset containing billions of records stored in a Parquet file. You need to load this dataset efficiently into an NVIDIA-accelerated RAPIDS environment for feature engineering.
Which of the following is the best approach?

A) Load the Parquet file into Dask and then convert it into a cuDF DataFrame for parallel processing
B) Convert the dataset into a CSV format and use cudf.read_csv() to load it into RAPIDS
C) Use pandas.read_parquet() to load the dataset and then convert it to a cuDF DataFrame
D) Load the Parquet file directly into a cuDF DataFrame using cudf.read_parquet()

Solutions: