Microsoft DP-100 Practice Test - Questions Answers, Page 6

Box 1: Positive skew

Positive skew values means the distribution is skewed to the right.

Box 2: Negative skew

Negative skewness values mean the distribution is skewed to the left.

References:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-elementary-statistics

Box 1: Yes

TabularExplainer calls one of the three SHAP explainers underneath (TreeExplainer, DeepExplainer, or KernelExplainer).

Box 2: Yes

To make your explanations and visualizations more informative, you can choose to pass in feature names and output class names if doing classification.

Box 3: No TabularExplainer automatically selects the most appropriate one for your use case, but you can call each of its three underlying explainers underneath (TreeExplainer, DeepExplainer, or KernelExplainer) directly.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-aml

Step 1: Select a model feature to be evaluated.

Step 2: Select a binary classification or regression model.

Register your models within Azure Machine Learning. For convenience, store the results in a dictionary, which maps the id of the registered model (a string in name:version format) to the predictor itself.

Example:

model_dict = {}

lr_reg_id = register_model("fairness_logistic_regression", lr_predictor)

model_dict[lr_reg_id] = lr_predictor

svm_reg_id = register_model("fairness_svm", svm_predictor)

model_dict[svm_reg_id] = svm_predictor

Step 3: Select a metric to be measured

Precompute fairness metrics.

Create a dashboard dictionary using Fairlearn's metrics package.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-fairness-aml

Box 1: Grid Search

Fairlearn open-source package provides postprocessing and reduction unfairness mitigation algorithms: ExponentiatedGradient, GridSearch, and ThresholdOptimizer.

Note: The Fairlearn open-source package provides postprocessing and reduction unfairness mitigation algorithms types:

Reduction: These algorithms take a standard black-box machine learning estimator (e.g., a LightGBM model) and generate a set of retrained models using a sequence of re-weighted training datasets.

Post-processing: These algorithms take an existing classifier and the sensitive feature as input.

Box 2: Demographic parity

The Fairlearn open-source package supports the following types of parity constraints: Demographic parity, Equalized odds, Equal opportunity, and Bounded group loss.

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml

C: Make sure your Windows system supports Hardware Virtualization Technology and that virtualization is enabled.

Ensure that hardware virtualization support is turned on in the BIOS settings. For example:

E: To run Docker, your machine must have a 64-bit operating system running Windows 7 or higher.

Reference:

https://docs.docker.com/toolbox/toolbox_install_windows/

https://blogs.technet.microsoft.com/canitpro/2015/09/08/step-by-step-enabling-hyper-v-for-use-on-windows-10/

In Azure Databricks, we can create two different types of clusters.

Standard, these are the default clusters and can be used with Python, R, Scala and SQL High-concurrency

Azure Databricks is fully integrated with Azure Data Factory.

Incorrect Answers:

D: Azure Container Instances is good for development or testing. Not suitable for production workloads.

Reference: https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/data-science-and-machine-learning

The Data Science Virtual Machine (DSVM) is a customized VM image on Microsoft's Azure cloud built specifically for doing data science. Caffe2 and Chainer are supported by DSVM. DSVM integrates with Azure Machine Learning.

Incorrect Answers:

B: Use Machine Learning Studio when you want to experiment with machine learning models quickly and easily, and the built-in machine learning algorithms are sufficient for your solutions.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

In the DSVM, your training models can use deep learning algorithms on hardware that's based on graphics processing units (GPUs).

PostgreSQL is available for the following operating systems: Linux (all recent distributions), 64-bit installers available for macOS (OS X) version 10.6 and newer - Windows (with installers available for 64-bit version; tested on latest versions and back to Windows 2012 R2.

Incorrect Answers:

B: The Azure Geo AI Data Science VM (Geo-DSVM) delivers geospatial analytics capabilities from Microsoft's Data Science VM. Specifically, this VM extends the AI and data science toolkits in the Data Science VM by adding ESRI's market-leading ArcGIS Pro Geographic Information System.

C, D: DLVM is a template on top of DSVM image. In terms of the packages, GPU drivers etc are all there in the DSVM image. Mostly it is for convenience during creation where we only allow DLVM to be created on GPU VM instances on

Azure.

Reference: https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview

Azure Cognitive Services expand on Microsoft's evolving portfolio of machine learning APIs and enable developers to easily add cognitive features - such as emotion and video detection; facial, speech, and vision recognition; and speech and language understanding - into their applications. The goal of Azure Cognitive Services is to help developers create applications that can see, hear, speak, understand, and even begin to reason. The catalog of services within Azure

Cognitive Services can be categorized into five main pillars - Vision, Speech, Language, Search, and Knowledge.

Reference: https://docs.microsoft.com/en-us/azure/cognitive-services/welcome