Question 1

A technology company is considering using Amazon Web Services (AWS) to support its growing application infrastructure and is exploring different cloud computing models. The team is particularly interested in Amazon Elastic Compute Cloud (EC2) to handle its scalable compute needs, as understanding this will help them determine how much control they have over the underlying infrastructure and how best to manage their resources.Which type of cloud computing does Amazon Elastic Compute Cloud (EC2) represent?

Options :

A : Network as a Service (NaaS)

B : Platform as a Service (PaaS)

C : Software as a Service (SaaS)

D : Infrastructure as a Service (IaaS)

Answer: D

Question 2

The admissions committee at an Ivy League university has noticed an increasing use of generative AI tools, such as language models, by applicants to draft their application essays. Concerned that some students may be using these tools to produce content that is not their original work, the committee aims to implement measures to detect the use of AI in essay creation. Their goal is to ensure that all submissions reflect the genuine thoughts and abilities of the applicants, thus maintaining the integrity and fairness of the admissions process.What specific issue is the admissions committee primarily trying to address by detecting the use of generative AI in application essays?

Options :

A : Plagiarism

B : Bias

C : Hallucination

D : Misinterpretation

Answer: A

Question 3

A company has developed an ML model to predict real estate sale prices. The company wants to deploy the model to make predictions without managing servers or infrastructure. Which solution meets these requirements?

Options :

A : Deploy the model on an Amazon EC2 instance.

B : Deploy the model on an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.

C : Deploy the model by using Amazon CloudFront with an Amazon S3 integration.

D : Deploy the model by using an Amazon SageMaker AI endpoint.

Answer: D

Question 4

A financial services company is exploring the use of AI to improve fraud detection and automate credit risk assessments. The data science team is evaluating whether to use traditional machine learning techniques or deep learning, depending on the complexity of the tasks and the size of the data involved. Understanding the key differences between deep learning and traditional machine learning will help the team choose the right approach.Which of the following would you suggest to the team? (Select two)

Options :

A : In traditional machine learning, a data scientist manually determines the set of relevant features that the software must analyze, whereas in deep learning, the data scientist gives only raw data to the software and the deep learning network derives the features by itself

B : Deep learning models are always faster to train than traditional machine learning models, regardless of the dataset size

C : Deep learning models do not require any data preprocessing, while traditional machine learning models require extensive data preprocessing

D : Traditional machine learning algorithms are only used for supervised learning tasks, whereas deep learning algorithms are only used for unsupervised learning tasks

E : Deep learning is a subset of machine learning that uses neural networks with many layers to learn from large amounts of data, while traditional machine learning algorithms often require feature extraction and can use various methods such as decision trees or support vector machines

Answer: A,E

Question 5

A company has built a solution by using generative AI. The solution uses large language models (LLMs) to translate training manuals from English into other languages. The company wants to evaluate the accuracy of the solution by examining the text generated for the manuals. Which model evaluation strategy meets these requirements?

Options :

A : Bilingual Evaluation Understudy (BLEU)

B : Root mean squared error (RMSE)

C : Recall-Oriented Understudy for Gisting Evaluation (ROUGE)

D : F1 score

Answer: A

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