Online Access Free Databricks-Machine-Learning-Professional Practice Test

Which of the following statements describes streaming with Spark as a model deployment strategy?

• A.The inference of batch processed records as soon as a trigger is hit
• B.The inference of batch processed records as soon as a Spark job is run
• C.The inference of incrementally processed records as soon as a Spark job is run
• D.The inference of incrementally processed records as soon as trigger is hit
• E.The inference of all types of records in real-time

A machine learning engineer is monitoring categorical input variables for a production machine learning application. The engineer believes that missing values are becoming more prevalent in more recent data for a particular value in one of the categorical input variables.
Which of the following tools can the machine learning engineer use to assess their theory?

• A.None of these
• B.Jenson-Shannon distance
• C.Kolmogorov-Smirnov (KS) test
• D.One-way Chi-squared Test
• E.Two-way Chi-squared Test

A machine learning engineer and data scientist are working together to convert a batch deployment to an always-on streaming deployment. The machine learning engineer has expressed that rigorous data tests must be put in place as a part of their conversion to account for potential changes in data formats.
Which of the following describes why these types of data type tests and checks are particularly important for streaming deployments?

• A.Because the streaming deployment is always on, all types of data must be handled without producing an error
• B.None of these statements
• C.Because the streaming deployment is always on, there is a need to confirm that the deployment can autoscale
• D.Because the streaming deployment is always on, there is no practitioner to debug poor model performance
• E.All of these statements

A machine learning engineer needs to deliver predictions of a machine learning model in real-time. However, the feature values needed for computing the predictions are available one week before the query time.
Which of the following is a benefit of using a batch serving deployment in this scenario rather than a real-time serving deployment where predictions are computed at query time?

• A.Batch serving has built-in capabilities in Databricks Machine Learning
• B.There is no advantage to using batch serving deployments over real-time serving deployments
• C.Testing is not possible in real-time serving deployments
• D.Computing predictions in real-time provides more up-to-date results
• E.Querying stored predictions can be faster than computing predictions in real-time

A data scientist has developed a model to predict ice cream sales using the expected temperature and expected number of hours of sun in the day. However, the expected temperature is dropping beneath the range of the input variable on which the model was trained.
Which of the following types of drift is present in the above scenario?

• A.None of these
• B.Prediction drift
• C.Feature drift
• D.Concept drift
• E.Label drift