Question 1

Which statement best defines the use of MLflow with Amazon SageMaker ?

Options :

A : Label data using human-in-the-loop

B : Manage machine learning experiments

C : Perform automatic model tuning

D : Leverage no-code ML

Answer: B

Question 2

A retail company is using an Amazon SageMaker machine learning model to analyze customer purchasing patterns and predict future buying behavior. The data scientists at the company regularly need to process datasets of less than 1 GB, such as daily sales records and customer interaction logs. The company does not require immediate responses and can afford some delay in receiving the analysis results, as the insights are primarily used for weekly strategy meetings.

Which inference method would be the most suitable for the company in this scenario?

Options :

A : Batch inference

B : Serverless inference

C : Asynchronous inference

D : Real-time inference

Answer: C

Question 3

A healthcare technology company is developing machine learning models to analyze both structured data, such as patient records, and unstructured data, such as medical images and clinical notes. The data science team is working on feature engineering to extract the most relevant information for the models but is aware that the process differs depending on whether the data is structured or unstructured. To ensure they approach each data type correctly, they need to understand the key differences in feature engineering tasks for structured versus unstructured data in machine learning.

What is a key difference in feature engineering tasks for structured data compared to unstructured data in the context of machine learning?

Options :

A : Feature engineering for structured data focuses on image recognition, whereas for unstructured data, it focuses on numerical data analysis

B : Feature engineering for structured data often involves tasks such as normalization and handling missing values, while for unstructured data, it involves tasks such as tokenization and vectorization

C : Feature engineering for structured data is not necessary as the data is already in a usable format, whereas for unstructured data, extensive preprocessing is always required

D : Feature engineering tasks for structured data and unstructured data are identical and do not vary based on data type

Answer: B

Question 4

A marketing analytics company is using generative AI to create custom reports and insights based on client data. The team is focused on improving the quality and relevance of the AI-generated content by refining how they interact with the model through prompts. To ensure the AI consistently produces high-quality and accurate outputs, they want to understand the best practices for crafting effective prompts that guide the model efficiently.

What are the key constituents of a good prompting technique in this context?

Options :

A : Instructions, Parameters, Input data, Output Indicato

B : Instructions, Hyperparameters, Input data, Output Indicator

C : Instructions, Context, Input data, Output Indicator

D : Hyperparameters, Context, Input data, Output Indicator

Answer: C

Question 5

Which of the following is mandatory when setting autotune configuration for Amazon SageMaker Automatic Model Tuning (AMT) ?

Options :

A : None

B : Number of jobs

C : Hyperparameter ranges

D : Tuning strategy

Answer: A

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