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Amazon AWS Certified AI Practitioner Sample Questions:

1. A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model.
The company needs to perform analysis on internal data and external data.
Which solution will meet these requirements?

A) Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.
B) Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.
C) Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.
D) Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.

2. A company has documents that are missing some words because of a database error. The company wants to build an ML model that can suggest potential words to fill in the missing text.
Which type of model meets this requirement?

A) BERT-based models
B) Clustering models
C) Prescriptive ML models
D) Topic modeling

3. A company wants to create an application by using Amazon Bedrock. The company has a limited budget and prefers flexibility without long-term commitment.
Which Amazon Bedrock pricing model meets these requirements?

A) On-Demand
B) Model customization
C) Provisioned Throughput
D) Spot Instance

4. Which feature of Amazon OpenSearch Service gives companies the ability to build vector database applications?

A) Ability to perform real-time analysis on streaming data
B) Support for geospatial indexing and queries
C) Integration with Amazon S3 for object storage
D) Scalable index management and nearest neighbor search capability

5. A company wants to develop an educational game where users answer questions such as the following: "A jar contains six red, four green, and three yellow marbles. What is the probability of choosing a green marble from the jar?" Which solution meets these requirements with the LEAST operational overhead?

A) Use unsupervised learning to create a model that will estimate probability density.
B) Use code that will calculate probability by using simple rules and computations.
C) Use supervised learning to create a regression model that will predict probability.
D) Use reinforcement learning to train a model to return the probability.

Solutions: