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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

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

2. A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?

A) Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.
B) Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.
C) Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.
D) Train a generative adversarial network (GAN) using PyTorch and then use the generated samples in RAPIDS AI without any additional processing.

3. You are working with a dataset consisting of 100 million records stored in a distributed system. The dataset includes numerical and categorical variables, requiring both exploratory data analysis (EDA) and machine learning model training. The processing time using traditional CPU-based methods is too slow.
Which of the following techniques would be the most effective acceleration method to handle this workload efficiently?

A) Scale up to a high-core-count CPU machine
B) Use RAPIDS cuDF for GPU-accelerated data processing
C) Store the dataset in a relational database and query it using SQL
D) Reduce the dataset to a smaller sample size before processin

4. You are working on a data science project where you need to process a large dataset containing
500 million records. You want to determine whether GPU acceleration would significantly improve performance.
Which of the following factors best indicates that you should use an accelerated computing solution like RAPIDS?

A) The dataset is a structured table with less than 100,000 records and can be handled efficiently with a Pandas DataFrame.
B) The dataset consists of simple arithmetic operations on a few columns and can be processed using vectorized NumPy operations.
C) The dataset is heavily structured but mainly requires text-based analysis using regex-based search and manipulation.
D) The dataset has high-dimensional sparse features and requires complex operations such as nearest neighbor search and clustering.

5. You are designing an ETL pipeline to process terabytes of financial transaction data in real time.
The pipeline consists of:
Extracting data from multiple sources (CSV, Parquet, and SQL databases), Transforming the data using operations such as filtering, joins, and aggregations, Loading the processed data into a data lake for analytics.
Given that you are using NVIDIA RAPIDS cuDF for GPU-accelerated ETL, which of the following approaches optimizes performance while ensuring scalability?

A) Use CPU-based ETL frameworks such as Apache Spark without GPU acceleration
B) Use cuDF to read and process the data in batches, leveraging Dask-cuDF for distributed computation when necessary
C) Convert cuDF DataFrames to Pandas DataFrames before performing transformations for compatibility
D) Load all data into a single, large cuDF DataFrame before performing transformations

Solutions: