Question 1

A Machine Learning Specialist is implementing a full Bayesian network on a dataset that describes public transit in New York City.One of the random variables is discrete and represents the number of minutes New Yorkers wait for a bus given that the buses cycle every 10 minutes, with a mean of 3 minutes.Which prior probability distribution should the ML Specialist use for this variable?

Options :

A : Normal distribution

B : Poisson distribution

C : Uniform distribution

D : Binomial distribution

Answer: D

Question 2

What characteristic of a problem makes ML an ideal solution?

Options :

A : The problem can be solved with traditional methods.

B : The problem involves complex and dynamic data.

C : The problem does not require scalability.

D : The problem requires simple logic.

Answer: B

Question 3

Why is explainability important in some contexts?

Options :

A : It improves the model-s accuracy

B : It enhances the model-s speed

C : It reduces the amount of training data needed

D : It ensures that decisions can be justified and understood

Answer: D

Question 4

A Machine Learning Specialist is preparing data for training on Amazon SageMaker.The Specialist is using one of the SageMaker built-in algorithms for the training.The dataset is stored in .CSV format and is transformed into a numpy.array, which appears to be negatively affecting the speed of the training.What should the Specialist do to optimize the data for training on SageMaker?

Options :

A : Use AWS Glue to compress the data into the Apache Parquet format.

B : Use the SageMaker hyperparameter optimization feature to automatically optimize the data.

C : Transform the dataset into the RecordIO protobuf format.

D : Use the SageMaker batch transform feature to transform the training data into a DataFrame.

Answer: C

Question 5

A company is deploying a new machine learning (ML) model in a production environment. The company is concerned that the ML model will drift over time, so the company creates a script to aggregate all inputs and predictions into a single file at the end of each day. The company stores the file as an object in an Amazon S3 bucket. The total size of the daily file is 100 GB. The daily file size will increase over time. Four times a year, the company samples the data from the previous 90 days to check the ML model for drift. After the 90-day period, the company must keep the files for compliance reasons. The company needs to use S3 storage classes to minimize costs. The company wants to maintain the same storage durability of the data. Which solution will meet these requirements?

Options :

A : Store the daily objects in the S3 One Zone-Infrequent Access (S3 One Zone-IA) storage class. Configure an S3 Lifecycle rule to move the objects to S3 Glacier Deep Archive after 90 days.

B : Store the daily objects in the S3 Standard-InfrequentAccess (S3 Standard-IA) storage class. Configure an S3 Lifecycle rule to move the objects to S3 Glacier Flexible Retrieval after 90 days.

C : Store the daily objects in the S3 Standard-InfrequentAccess (S3 Standard-IA) storage class. Configure an S3 Lifecycle rule to move the objects to S3 Glacier Deep Archive after 90 days.

D : Store the daily objects in the S3 One Zone-Infrequent Access (S3 One Zone-IA) storage class. Configure an S3 Lifecycle rule to move the objects to S3 Glacier Flexible Retrieval after 90 days.

Answer: C

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