DP-100 Online Practice Questions and Answers

Correct Answer:

Box 1: JSON containing an OAuth bearer token

Specify your authentication header in the request. To run the pipeline from the REST endpoint, you need an OAuth2 Bearer-type authentication header.

Box 2: JSON containing the experiment name

Add a JSON payload object that has the experiment name.

Example:

rest_endpoint = published_pipeline.endpoint

response = requests.post(rest_endpoint,

headers=auth_header,

json={"ExperimentName": "batch_scoring",

"ParameterAssignments": {"process_count_per_node": 6}}) run_id = response.json()["Id"]

Box 3: JSON containing the run ID

Make the request to trigger the run. Include code to access the Id key from the response dictionary to get the value of the run ID.

Correct Answer: B

Need to attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target

Correct Answer: BCD

B: You can use an Azure Resource Manager template to create a workspace for Azure Machine Learning.

Example:

{"type": "Microsoft.MachineLearningServices/workspaces", ...

C: You can create a workspace for Azure Machine Learning with Azure CLI Install the machine learning extension.

Create a resource group: az group create --name --location

To create a new workspace where the services are automatically created, use the following command: az ml workspace create -w -g

D: You can create and manage Azure Machine Learning workspaces in the Azure portal.

1. Sign in to the Azure portal by using the credentials for your Azure subscription.

2. In the upper-left corner of Azure portal, select + Create a resource.

3. Use the search bar to find Machine Learning.

4. Select Machine Learning.

5. In the Machine Learning pane, select Create to begin.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-workspace-template https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace-cli https://docs.microsoft.com/en-us/azure/machine-learning/how-to-manage-workspace

Correct Answer: A

The two steps are present: process_step and train_step

Data_input correctly references the data in the data store.

Note:

Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.

PipelineData objects are also used when constructing Pipelines to describe step dependencies. To specify that a step requires the output of another step as input, use a PipelineData object in the constructor of both steps.

For example, the pipeline train step depends on the process_step_output output of the pipeline process step:

from azureml.pipeline.core import Pipeline, PipelineData from azureml.pipeline.steps import PythonScriptStep

datastore = ws.get_default_datastore()

process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output],

outputs=[process_step_output],

compute_target=aml_compute,

source_directory=process_directory)

train_step = PythonScriptStep(script_name="train.py",

arguments=["--data_for_train", process_step_output],

inputs=[process_step_output],

compute_target=aml_compute,

source_directory=train_directory)

pipeline = Pipeline(workspace=ws, steps=[process_step, train_step])

Reference:

https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata? view=azure-ml-py

Correct Answer: A

Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values.

Note: Multivariate imputation by chained equations (MICE), sometimes called "fully conditional specification" or "sequential regression multiple imputation" has emerged in the statistical literature as one principled method of addressing missing data. Creating multiple imputations, as opposed to single imputations, accounts for the statistical uncertainty in the imputations. In addition, the chained equations approach is very flexible and can handle variables of varying types (e.g., continuous or binary) as well as complexities such as bounds or survey skip patterns.

References: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3074241/

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/clean-missing-data

Correct Answer: D

Receiver operating characteristic (or ROC) is a plot of the correctly classified labels vs. the incorrectly classified labels for a particular model. Incorrect Answers:

A: A violin plot is a visual that traditionally combines a box plot and a kernel density plot.

B: Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point.

C: A scatter plot graphs the actual values in your data against the values predicted by the model. The scatter plot displays the actual values along the X-axis, and displays the predicted values along the Y-axis. It also displays a line that illustrates the perfect prediction, where the predicted value exactly matches the actual value.

References: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#confusion- matrix

Correct Answer: B

Define paths with two file paths instead.

Use Dataset.Tabular_from_delimeted as the data isn't cleansed.

Reference:

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

Correct Answer: AE

The run Class get_all_logs method downloads all logs for the run to a directory.

The run Class get_details gets the definition, status information, current log files, and other details of the run.

Incorrect Answers:

B: The run get_file_names list the files that are stored in association with the run.

Reference: https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.run(class)

Correct Answer: D

azureml.core.Model.properties:

Dictionary of key value properties for the Model. These properties cannot be changed after registration, however new key value pairs can be added.

Reference:

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

Correct Answer: B

Correct Answer: AC

C: Python main function is a starting point of any program. When the program is run, the python interpreter runs the code sequentially. Main function is executed only when it is run as a Python program.

A: Refactoring, code style and testing

The first step is to modularise the notebook into a reasonable folder structure, this effectively means to convert files from .ipynb format to .py format, ensure each script has a clear distinct purpose and organise these files in a coherent way.

Once the project is nicely structured we can tidy up or refactor the code.

Reference: https://www.guru99.com/learn-python-main-function-with-examples-understand-main.html https://towardsdatascience.com/from-jupyter-notebook-to-deployment-a-straightforward-example-1838c203a437

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

Correct Answer: B

Discrete hyperparameters (as this is Grid Sample, see note below)

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

one or more comma-separated values

a range object

any arbitrary list object

Python

from azure.ai.ml.sweep import Choice

command_job_for_sweep = command_job(

batch_size=Choice(values=[16, 32, 64, 128]),

number_of_hidden_layers=Choice(values=range(1,5)),

)

In this case, batch_size one of the values [16, 32, 64, 128] and number_of_hidden_layers takes one of the values [1, 2, 3, 4].

The following advanced discrete hyperparameters can also be specified using a distribution:

QUniform(min_value, max_value, q) - Returns a value like round(Uniform(min_value, max_value) / q) * q

QLogUniform(min_value, max_value, q) - Returns a value like round(exp(Uniform(min_value, max_value)) / q) * q

QNormal(mu, sigma, q) - Returns a value like round(Normal(mu, sigma) / q) * q

*-> QLogNormal(mu, sigma, q) - Returns a value like round(exp(Normal(mu, sigma)) / q) * q

Note: Grid sampling

Grid sampling can only be employed when all hyperparameters are discrete, and is used to try every possible combination of parameters in the search space.

For example, in the following code example, grid sampling is used to try every possible combination of discrete batch_size and learning_rate value:

from azureml.train.hyperdrive import GridParameterSampling, choice

param_space = {

'--batch_size': choice(16, 32, 64),

'--learning_rate': choice(0.01, 0.1, 1.0)

}

param_sampling = GridParameterSampling(param_space)

Reference:

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

Correct Answer: A

Define the search space

Tune hyperparameters by exploring the range of values defined for each hyperparameter.

Hyperparameters can be discrete or continuous, and has a distribution of values described by a parameter expression.

Discrete hyperparameters

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

one or more comma-separated values

a range object

any arbitrary list object

Example:

from azure.ai.ml.sweep import Choice

command_job_for_sweep = command_job(

batch_size=Choice(values=[16, 32, 64, 128]),

number_of_hidden_layers=Choice(values=range(1,5)),

)

Reference:

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

Correct Answer: D

Logging files

In general, files in MLflow are called artifacts. You can log artifacts in multiple ways in Mlflow:

*

Log text in a text file

mlflow.log_text("text string", "notes.txt")

Text is persisted inside of the run in a text file with name notes.txt.

Incorrect:

*

mlflow.log.dict

Log dictionaries as JSON and YAML files

mlflow.log_dict(dictionary, "file.yaml"

dictionary is a dictionary object containing all the structure that you want to persist as JSON or YAML file.

*

mlflow.log_artifact

Log a trivial file already existing

mlflow.log_artifact("path/to/file.pkl")

Files are always logged in the root of the run. If artifact_path is provided, then the file is logged in a folder as indicated in that parameter.

*

Log all the artifacts in an existing folder

mlflow.log_artifacts("path/to/folder")

Folder structure is copied to the run, but the root folder indicated is not included.

Reference:

https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-view-metrics