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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is working on a dataset where the numerical features have different ranges, and they need to ensure uniformity across features before training a machine learning model.
Which of the following approaches, utilizing NVIDIA technologies, would best achieve this goal?

A) Use cuML's StandardScaler() to transform the features to have zero mean and unit variance.
B) Apply cuDF's normalize() function to scale each feature between 0 and 1.
C) Use cuML's PCA to directly remove the need for standardization by reducing dimensionality.
D) Apply cuML's RobustScaler() to center the data using median and scale using the interquartile range.

2. When utilizing GPU instances in a cloud environment for data science, which of the following are common considerations? (Select two)

A) GPUs are suitable for both training and inference tasks but not for preprocessing
B) GPU performance can be degraded by network latency in distributed training
C) Cloud providers offer dedicated GPU instances for high-performance needs
D) Cost-effective GPU instances always provide the best performance

3. Which of the following is the most efficient method for processing big data in a distributed environment using NVIDIA technologies?

A) Using cloud-based, non-GPU infrastructure for data processing
B) Using a single-node GPU-based solution for data processing.
C) Relying on traditional CPU-only processing frameworks for big data tasks.
D) Utilizing a distributed data processing framework like Apache Spark with GPU acceleration from the NVIDIA RAPIDS library.

4. A retail company needs to design a real-time ETL workflow to process millions of sales records per second from multiple sources. The data needs to be cleaned, aggregated, and stored for real-time analytics.
Which NVIDIA-powered solution is best suited for this workload?

A) Use NVIDIA Spark RAPIDS to accelerate ETL processing within Apache Spark on GPUs
B) Use NumPy to perform array-based operations on sales data before inserting into a database
C) Perform ETL with cuGraph to model sales data as a graph before storage
D) Use standard Apache Spark on CPU clusters and move transformed data to GPU storage

5. In the context of the CRISP-DM process, you are tasked with building a machine learning model on a large dataset. You decide to leverage GPU resources to accelerate training.
Which of the following steps in the CRISP-DM process are most likely to benefit from the use of GPU resources in the cloud environment? (Select two)

A) Business Understanding
B) Data Understanding
C) Data Exploration
D) Model Training and Evaluation
E) Model Deployment and Monitoring

Solutions: