DP-100: Designing and Implementing a Data Science Solution on Azure

14%

142 QUESTIONS AS TOTAL
Question 21

You have published a pipeline that you want to run every week.
You plan to use the Schedule.create method to create the schedule.
What kind of object must you create first to configure how frequently the pipeline runs?
Datastore
PipelineParameter
ScheduleRecurrance




Answer is ScheduleRecurrance. You need a ScheduleRecurrance object to create a schedule that runs at a regular interval.
Question 22

You have trained a model using the Python SDK for Azure Machine Learning.
You want to deploy the model as a containerized real-time service with high scalability and security.
What kind of compute should you create to host the service?
An Azure Kubernetes Services (AKS) inferencing cluster.
A compute instance with GPUs.
A training cluster with multiple nodes.




Answer is An Azure Kubernetes Services (AKS) inferencing cluster. You should use an AKS cluster to deploy a model as a scalable, secure, containerized service.
Question 23

You are deploying a model as a real-time inferencing service. What functions must the entry script for the service include?
main() and predict(raw_data)
load() and score(raw_data)
init() and run(raw_data)




Answer is init() and run(raw_data) You need to implement init and run functions in the entry (scoring) script.
Question 24

You are creating a batch inferencing pipeline that you want to use to predict new values for a large volume of data files?
You want the pipeline to run the scoring script on multiple nodes and collate the results.
What kind of step should you include in the pipeline?
PythonScriptStep
ParallelRunStep
AdlaStep




Answer is ParallelRunStep. You should use a ParallelRunStep step to run the scoring script in parallel.
Question 25

You have configured the step in your batch inferencing pipeline with an output_action="append_row" property.
In which file should you look for the batch inferencing results?
output.txt
parallel_run_step.txt
stdoutlogs.txt




Answer is parallel_run_step.txt Using the append_row output action causes the results from the ParallelRunStep step to be collated in a file named parallel_run_step.txt.
Question 26

You plan to use hyperparameter tuning to find optimal discrete values for a set of hyperparameters.
You want to try every possible combination of a set of specified discrete values.
Which kind of sampling should you use?
Grid Sampling
Random Sampling
Bayesian Sampling




Answer is Grid Sampling. You should use a Grid sampling to try every combination of discrete hyperparameter values.
Question 27

You are using hyper parameter tuning to train an optimal model. Your training script calculates the area under the curve (AUC) metric for the trained model like this: 
y_scores = model.predict_proba(X_test)
auc = roc_auc_score(y_test,y_scores[:,1])
You define the hyperdrive configuration like this: 
hyperdrive = HyperDriveConfig(estimator=sklearn_estimator,
hyperparameter_sampling=grid_sampling,
policy=None,
primary_metric_name='AUC',
primary_metric_goal=PrimaryMetricGoal.MAXIMIZE,
max_total_runs=6,
max_concurrent_runs=4)
Which code should you add to the training script?
run.log('Accuracy', np.float(auc))
print(auc)
run.log('AUC', np.float(auc))




Answer is run.log('Accuracy', np.float(auc))

Your script needs to log the primary metric using the same name as specified in the hyperdrive config.
Question 28

You are using automated machine learning to train a model that predicts the species of an iris based on its petal and sepal measurements.
Which kind of task should you specify for automated machine learning?
Regression
Classification
Forecasting




Answer is Classification. Predicting a class requires a classification task.
Question 29

You have submitted an automated machine learning run using the Python SDk for Azure Machine Learning.
When the run completes, which method of the run object should you use to retrieve the best model?
load_model()
get_output()
get_metrics()




Answer is get_output(). The get_output method of an automated machine learning run returns the best mode and the child run that trained it.
Question 30

You have trained a model, and you want to quantify the influence of each feature on a specific individual prediction.
What kind of feature importance should you examine?
Global feature importance
Local feature importance




Answer is Local feature importance. Local importance indicates the influence of features on a specific prediction. Global importance gives an overall indication of feature influence.
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