Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 241:

  You use the Azure Machine Learning Python SDK to define a pipeline that consists of multiple steps.

  When you run the pipeline, you observe that some steps do not run. The cached output from a previous run is used instead.

  You need to ensure that every step in the pipeline is run, even if the parameters and contents of the source directory have not changed since the previous run.

  What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. Use a PipelineData object that references a datastore other than the default datastore.

  B. Set the regenerate_outputs property of the pipeline to True.

  C. Set the allow_reuse property of each step in the pipeline to False.

  D. Restart the compute cluster where the pipeline experiment is configured to run.

  E. Set the outputs property of each step in the pipeline to True.

  Correct Answer: BC

  B: If regenerate_outputs is set to True, a new submit will always force generation of all step outputs, and disallow data reuse for any step of this run. Once this run is complete, however, subsequent runs may reuse the results of this run.

  C: Keep the following in mind when working with pipeline steps, input/output data, and step reuse.

  If data used in a step is in a datastore and allow_reuse is True, then changes to the data change won't be detected. If the data is uploaded as part of the snapshot (under the step's source_directory), though this is not recommended, then the

  hash will change and will trigger a rerun.

  Reference:

  https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinestep

  https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb

• Question 242:

  You use the following Python code in a notebook to deploy a model as a web service:

  from azureml.core.webservice import AciWebservice

  from azureml.core.model import InferenceConfig

  inference_config = InferenceConfig(runtime='python', source_directory='model_files', entry_script='score.py', conda_file='env.yml')

  deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)

  service = Model.deploy(ws, 'my-service', [model], inference_config, deployment_config)

  service.wait_for_deployment(True)

  The deployment fails.

  You need to use the Python SDK in the notebook to determine the events that occurred during service deployment an initialization.

  Which code segment should you use?

  A. service.state

  B. service.get_logs()

  C. service.serialize()

  D. service.environment

  Correct Answer: B

  The first step in debugging errors is to get your deployment logs. In Python: service.get_logs()

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment

• Question 243:

  You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and Theano. You need to select a pre configured DSVM to support the framework. Which of following should you create?

  A. Data Science Virtual Machine for Linux (Ubuntu)

  B. Data Science Virtual Machine for Windows 2012

  C. Data Science Virtual Machine for Windows 2016

  D. Data Science Virtual Machine for Linux (CentOS)

  Correct Answer: A

• Question 244:

  You plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:

  from azureml.core import Run import pandas as pd run = Run.get_context() data = pd.read_csv('data.csv') label_vals = data['label'].unique() # Add code to record metrics here run.complete()

  The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.

  You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.

  Solution: Replace the comment with the following code:

  run.log_table('Label Values', label_vals)

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  Instead use the run_log function to log the contents in label_vals:

  for label_val in label_vals: run.log('Label Values', label_val)

  Reference: https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai

• Question 245:

  You plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:

  from azureml.core import Run import pandas as pd run = Run.get_context() data = pd.read_csv('data.csv') label_vals = data['label'].unique() # Add code to record metrics here run.complete()

  The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.

  You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.

  Solution: Replace the comment with the following code:

  for label_val in label_vals:

  run.log('Label Values', label_val)

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: A

  The run_log function is used to log the contents in label_vals:

  for label_val in label_vals: run.log('Label Values', label_val)

  Reference: https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai

• Question 246:

  Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while

  others might not have a correct solution.

  After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

  You train and register a machine learning model.

  You plan to deploy the model as a real-time web service. Applications must use key-based authentication to use the model.

  You need to deploy the web service.

  Solution:

  Create an AciWebservice instance.

  Set the value of the ssl_enabled property to True.

  Deploy the model to the service.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  Instead use only auth_enabled = TRUE

  Note: Key-based authentication.

  Web services deployed on AKS have key-based auth enabled by default. ACI-deployed services have key-based auth disabled by default, but you can enable it by setting auth_enabled = TRUE when creating the ACI web service. The

  following is an example of creating an ACI deployment configuration with key-based auth enabled.

  deployment_config <- aci_webservice_deployment_config(cpu_cores = 1, memory_gb = 1, auth_enabled = TRUE)

  Reference: https://azure.github.io/azureml-sdk-for-r/articles/deploying-models.html

• Question 247:

  Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while

  others might not have a correct solution.

  After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

  You train and register a machine learning model.

  You plan to deploy the model as a real-time web service. Applications must use key-based authentication to use the model.

  You need to deploy the web service.

  Solution:

  Create an AksWebservice instance.

  Set the value of the auth_enabled property to True.

  Deploy the model to the service.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: A

  Key-based authentication.

  Web services deployed on AKS have key-based auth enabled by default. ACI-deployed services have key-based auth disabled by default, but you can enable it by setting auth_enabled = TRUE when creating the ACI web service. The

  following is an example of creating an ACI deployment configuration with key-based auth enabled.

  deployment_config <- aci_webservice_deployment_config(cpu_cores = 1, memory_gb = 1, auth_enabled = TRUE)

  Reference: https://azure.github.io/azureml-sdk-for-r/articles/deploying-models.html

• Question 248:

  You plan to use a Python script to run an Azure Machine Learning experiment. The script creates a reference to the experiment run context, loads data from a file, identifies the set of unique values for the label column, and completes the experiment run:

  from azureml.core import Runimport pandas as pdrun = Run.get_context()data = pd.read_csv('data.csv')label_vals = data['label'].unique()# Add code to record metrics here run.complete()

  The experiment must record the unique labels in the data as metrics for the run that can be reviewed later.

  You must add code to the script to record the unique label values as run metrics at the point indicated by the comment.

  Solution: Replace the comment with the following code:

  run.upload_file('outputs/labels.csv', './data.csv')

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  label_vals has the unique labels (from the statement label_vals = data['label'].unique()), and it has to be logged.

  Note:

  Instead use the run_log function to log the contents in label_vals:

  for label_val in label_vals: run.log('Label Values', label_val)

  Reference: https://www.element61.be/en/resource/azure-machine-learning-services-complete-toolbox-ai

• Question 249:

  You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.

  You must use Hyperdrive to try combinations of the following hyperparameter values:

  1.

  learning_rate: any value between 0.001 and 0.1

  2.

  batch_size: 16, 32, or 64

  You need to configure the search space for the Hyperdrive experiment.

  Which two parameter expressions should you use? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. a choice expression for learning_rate

  B. a uniform expression for learning_rate

  C. a normal expression for batch_size

  D. a choice expression for batch_size

  E. a uniform expression for batch_size

  Correct Answer: BD

  B: Continuous hyperparameters are specified as a distribution over a continuous range of values.

  Supported distributions include:

  uniform(low, high) - Returns a value uniformly distributed between low and high

  D: Discrete hyperparameters are specified as a choice among discrete values. choice can be:

  1.

  one or more comma-separated values

  2.

  a range object

  3.

  any arbitrary list object

  Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

• Question 250:

  Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.

  After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

  You create an Azure Machine Learning service datastore in a workspace. The datastore contains the following files:

  1.

  /data/2018/Q1.csv

  2.

  /data/2018/Q2.csv

  3.

  /data/2018/Q3.csv

  4.

  /data/2018/Q4.csv

  5.

  /data/2019/Q1.csv

  All files store data in the following format:

  id,f1,f2,I 1,1,2,0 2,1,1,1 3,2,1,0 4,2,2,1

  You run the following code:

  

  You need to create a dataset named training_data and load the data from all files into a single data frame by using the following code:

  

  Solution: Run the following code:

  

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  Use two file paths.

  Use Dataset.Tabular_from_delimeted, instead of Dataset.File.from_files as the data isn't cleansed.

  Note:

  A FileDataset references single or multiple files in your datastores or public URLs. If your data is already cleansed, and ready to use in training experiments, you can download or mount the files to your compute as a FileDataset object.

  A TabularDataset represents data in a tabular format by parsing the provided file or list of files. This provides you with the ability to materialize the data into a pandas or Spark DataFrame so you can work with familiar data preparation and

  training libraries without having to leave your notebook. You can create a TabularDataset object from .csv, .tsv, .parquet, .jsonl files, and from SQL query results.

  Reference:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-register-datasets