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Exam 70-774 toward MCSA


Overview

Here are materials I’ve come across for learning enough to pass Microsoft’s Azure Machine Learning certification exam 70-774 - 37 questions at a VUE center for $165.

The exam began beta on Feb 17, 2017.

Since Machine Learning often involves processing a lot of data, passing this exam as well as “Exam 70-773: Analyzing Big Data with Microsoft R” gets you an MCSA: “Microsoft Certified Solutions Associate: Machine Learning certification”, demonstrating your expertise in operationalizing Microsoft Azure machine learning and Big Data with R Server and SQL R Services.

Candidates for this exam are data scientists or analysts who use Azure cloud services to build and deploy intelligent solutions. Candidates have a good understanding of Azure data services and machine learning and are familiar with common data science processes such as filtering and transforming data sets, model estimation, and model evaluation. Candidates for this exam should have experience publishing effective APIs for knowledge intelligence.

MCSA Bundle

The http://learnanalytics.microsoft.com/home/index team has a GitHub.io page

PROTIP: The 336 page reference book published Feb 2018 for the exam is $32 from Microsoft, but $17.27 on Kindle.

Its authors - Ginger Grant, Julio Granados, Guillermo Fernández, Pau Sempere, Javier Torrenteras also wrote the book on 70-773.

Registration info for 70-773 and 70-774. It says:

I mention this duo because vendors offer bundles such as:

  • PDF & Practice Exam from CertificationGenie
  • https://www.netcomlearning.com/certifications/610/MCSA-Machine-Learning-training.html
  • https://www.pass4itsure.com/70-774.html

Topics

The top-level:

A. Prepare data for analysis in Azure Machine Learning and export from Azure Machine Learning
B. Develop machine learning models
C. Operationalize and manage Azure Machine Learning services
D. Use other services for machine learning

Objectives

This provides a deeper list:

A. Prepare Data and Analytics in Azure Machine Learning and Export from Azure Machine Learning

B. Develop Machine Learning Models

C. Operationalize and Manage Azure Machine Learning services

D. Use Other Services for Machine Learning

Tasks

Microsoft’s official reference page on the exam lists what tasks are being tested.

PROTIP: Save this page and check off what you’re able to do as you learn each one.

A. Prepare Data for Analysis in Azure Machine Learning and Export from Azure Machine Learning

Import and export data to and from Azure Machine Learning

  • [_] Import and export data to and from Azure Blob storage
  • [_] import and export data to and from Azure SQL Database
  • [_] import and export data via Hive Queries
  • [_] import data from a website
  • [_] import data from on-premises SQL

Explore and summarize data

  • [_] Create univariate summaries
  • [_] create multivariate summaries
  • [_] visualize univariate distributions
  • [_] use existing Microsoft R or Python notebooks for custom summaries and custom visualizations
  • [_] use zip archives to import external packages for R or Python
  • [_] Cleanse data for Azure Machine Learning
  • [_] Apply filters to limit a dataset to the desired rows
  • [_] identify and address missing data
  • [_] identify and address outliers
  • [_] remove columns and rows of datasets

Perform feature engineering

  • [_] Merge multiple datasets by rows or columns into a single dataset by columns
  • [_] merge multiple datasets by rows or columns into a single dataset by rows
  • [_] add columns that are combinations of other columns
  • [_] manually select and construct features for model estimation
  • [_] automatically select and construct features for model estimation
  • [_] reduce dimensions of data through principal component analysis (PCA)
  • [_] manage variable metadata
  • [_] select standardized variables based on planned analysis

B. Develop Machine Learning Models

Select an appropriate algorithm or method

  • My notes on algorithms

  • [_] Select an appropriate algorithm for predicting continuous label data
  • [_] select an appropriate algorithm for supervised versus unsupervised scenarios
  • [_] identify when to select R versus Python notebooks
  • [_] identify an appropriate algorithm for grouping unlabeled data
  • [_] identify an appropriate algorithm for classifying label data
  • [_] select an appropriate ensemble

Initialize and train appropriate models

  • [_] Tune hyperparameters manually
  • [_] tune hyperparameters automatically
  • [_] split data into training and testing datasets, including using routines for cross-validation
  • [_] build an ensemble using the stacking method

Validate models

  • [_] Score and evaluate models,
  • [_] select appropriate evaluation metrics for clustering,
  • [_] select appropriate evaluation metrics for classification,
  • [_] select appropriate evaluation metrics for regression,
  • [_] use evaluation metrics to choose between Machine Learning models,
  • [_] compare ensemble metrics against base models

C. Operationalize and Manage Azure Machine Learning Services

Deploy models using Azure Machine Learning

  • [_] Publish a model developed inside Azure Machine Learning
  • [_] publish an externally developed scoring function using an Azure Machine Learning package
  • [_] use web service parameters, create and publish a recommendation model
  • [_] create and publish a language understanding model

Manage Azure Machine Learning projects and workspaces

  • [_] Create projects and experiments
  • [_] add assets to a project
  • [_] create new workspaces
  • [_] invite users to a workspace, switch between different workspaces
  • [_] create a Jupyter notebook that references an intermediate dataset

Consume Azure Machine Learning models

  • [_] Connect to a published Machine Learning web service
  • [_] consume a published Machine Learning model programmatically using a batch execution service
  • [_] consume a published Machine Learning model programmatically using a request response service
  • [_] interact with a published Machine Learning model using Microsoft Excel
  • [_] publish models to the marketplace

Consume exemplar Cognitive Services APIs

  • [_] Consume Vision APIs to process images
  • [_] consume Language APIs to process text
  • [_] consume Knowledge APIs to create recommendations

D. Use Other Services for Machine Learning

Build and use neural networks with the Microsoft Cognitive Toolkit

  • [_] Use N-series VMs for GPU acceleration
  • [_] build and train a three-layer feed forward neural network
  • [_] determine when to implement a neural network

Streamline development by using existing resources

  • [_] Clone template experiments from Cortana Intelligence Gallery
  • [_] use Cortana Intelligence Quick Start to deploy resources
  • [_] use a data science VM for streamlined development

Perform data sciences at scale by using HDInsights

  • [_] Deploy the appropriate type of HDI cluster
  • [_] perform exploratory data analysis by using Spark SQL
  • [_] build and use Machine Learning models with Spark on HDI
  • [_] build and use Machine Learning models using MapReduce
  • [_] build and use Machine Learning models using Microsoft R Server

Perform database analytics by using SQL Server R Services on Azure

  • [_] Deploy a SQL Server 2016 Azure VM
  • [_] configure SQL Server to allow execution of R scripts
  • [_] execute R scripts inside T-SQL statements

Study materials

I’ve rearranged Daniel Calbimonte’s massive list and others into my sequence below.

Free ebooks

Let’s start

Premium Video Courses

Videos

Follow this machine learning tutorial to use Azure Machine Learning Studio to create a linear regression model that predicts the price of an automobile based on different variables such as make and technical specifications. Then iterate on a simple predictive analytics experiment.

VIDEO: Hands-On with Azure Machine Learning

Machine Learning Algorthms - Part 1

this video: https://studio.azureml.net/?selectAccess=true&o=2

More

This is one of a series on AI, Machine Learning, Deep Learning, Robotics, and Analytics:

  1. Tableau Data Visualization
  2. Regression calculation and visualization using Excel

  3. AI Ecosystem
  4. Machine Learning
  5. Testing AI

  6. Microsoft’s AI
  7. Microsoft’s Azure Machine Learning Algorithms
  8. Microsoft’s Azure Machine Learning tutorial
  9. Microsoft’s Azure Machine Learning certification

  10. Python installation
  11. Juypter notebooks processing Python for humans

  12. Image Processing
  13. Amazon Lex text to speech

  14. Code Generation