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Databricks Machine Learning Professional Dumps
1. Which of the following MLflow Model Registry use cases requires the
use of an HTTP Webhook?
A.Starting a testing job when a new model is registered
B.Updating data in a source table for a Databricks SQL dashboard when
a model version transitions to the Production stage
C.Sending an email alert when an automated testing Job fails
D.None of these use cases require the use of an HTTP Webhook
E.Sending a message to a Slack channel when a model version transitions
stages
Answer: B
Databricks Machine Learning Professional Dumps
2. Which of the following lists all of the model stages are available in the
MLflow Model Registry?
A.Development. Staging. Production
B.None. Staging. Production
C.Staging. Production. Archived
D.None. Staging. Production. Archived
E.Development. Staging. Production. Archived
Answer: A
Databricks Machine Learning Professional Dumps
3. 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.Computing predictions in real-time provides more up-to-date results
D.Testing is not possible in real-time serving deployments
E.Querying stored predictions can be faster than computing predictions in real-time
Answer: A
Databricks Machine Learning Professional Dumps
4. Which of the following describes the purpose of the context parameter in the predict
method of Python models for MLflow?
A.The context parameter allows the user to specify which version of the registered MLflow
Model should be used based on the given application's current scenario
B.The context parameter allows the user to document the performance of a model after it
has been deployed
C.The context parameter allows the user to include relevant details of the business case to
allow downstream users to understand the purpose of the model
D.The context parameter allows the user to provide the model with completely custom ifelse logic for the given application's current scenario
E.The context parameter allows the user to provide the model access to objects like
preprocessing models or custom configuration files
Answer: A
Databricks Machine Learning Professional Dumps
5. A machine learning engineering team has written predictions computed in a batch job to
a Delta table for querying. However, the team has noticed that the querying is running
slowly. The team has already tuned the size of the data files. Upon investigating, the team
has concluded that the rows meeting the query condition are sparsely located throughout
each of the data files.
Based on the scenario, which of the following optimization techniques could speed up the
query by colocating similar records while considering values in multiple columns?
A.Z-Ordering
B.Bin-packing
C.Write as a Parquet file
D.Data skipping
E.Tuning the file size
Answer: E
Databricks Machine Learning Professional Dumps
6. Which of the following Databricks-managed MLflow capabilities is a
centralized model store?
A.Models
B.Model Registry
C.Model Serving
D.Feature Store
E.Experiments
Answer: C
Databricks Machine Learning Professional Dumps
7. A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model.
They have custom preprocessing that needs to be completed on feature variables prior to fitting
the model or computing predictions using that model. They decide to wrap this preprocessing in
a custom model class ModelWithPreprocess, where the preprocessing is performed when calling
fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as
a pyfunc model.
Which of the following is a benefit of this approach when loading the logged pyfunc model for
downstream deployment?
A.The pvfunc model can be used to deploy models in a parallelizable fashion
B.The same preprocessing logic will automatically be applied when calling fit
C.The same preprocessing logic will automatically be applied when calling predict
D.This approach has no impact when loading the logged Pvfunc model for downstream
deployment
E.There is no longer a need for pipeline-like machine learning objects
Answer: E
Databricks Machine Learning Professional Dumps
8. Which of the following describes label drift?
A. Label drift is when there is a change in the distribution of the
predicted target given by the model
B. None of these describe label drift
C. Label drift is when there is a change in the distribution of an input
variable
D. Label drift is when there is a change in the relationship between input
variables and target variables
E. Label drift is when there is a change in the distribution of a target
variable
Answer: C
Databricks Machine Learning Professional Dumps
9. Which of the following is a simple statistic to monitor for categorical
feature drift?
A. Mode
B. None of these
C. Mode, number of unique values, and percentage of missing values
D. Percentage of missing values
E. Number of unique values
Answer: C
Databricks Machine Learning Professional Dumps
10. Which of the following is a probable response to identifying drift in a
machine learning application?
A. None of these responses
B. Retraining and deploying a model on more recent data
C. All of these responses
D. Rebuilding the machine learning application with a new label variable
E. Sunsetting the machine learning application
Answer: A