70-475

70-475

Exam Code: 70-475
Exam Name: Designing and Implementing Big Data Analytics Solutions
Questions: 60 Q&As
Updated: 2018-11-04
Price: $ 59 / $ 79
Demo: 70-475 DEMO (Free)

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The latest actual 70-475 questions and answers from ISLEVER. Everything you need to prepare and get best score at 70-475 exam easily and quickly. "Designing and Implementing Big Data Analytics Solutions" exam engine covers all the knowledge points of the real Microsoft exam.

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Technology: Microsoft Azure

Credit toward certification: MCP, MCSA, MCSE


Design big data batch processing and interactive solutions (30–35%)

Ingest data for batch and interactive processing

Ingest from cloud-born or on-premises data, store data in Microsoft Azure Data Lake, store data in Azure BLOB Storage, perform a one-time bulk data transfer, perform routine small writes on a continuous basis

Design and provision compute clusters

Select compute cluster type, estimate cluster size based on workload

Design for data security

Protect personally identifiable information (PII) data in Azure, encrypt and mask data, implement role-based security, implement row-based security

Design for batch processing

Select appropriate language and tool, identify formats, define metadata, configure output

Design big data real-time processing solutions (30–35%)

Ingest data for real-time processing

Select data ingestion technology, design partitioning scheme, design row key of event tables in HBase

Design and provision compute resources

Select streaming technology in Azure, select real-time event processing technology, select real-time event storage technology, select streaming units, configure cluster size, select the right technology for business requirements, assign appropriate resources for HBase clusters

Design for Lambda architecture

Identify application of Lambda architecture, utilize streaming data to draw business insights in real time, utilize streaming data to show trends in data in real time, utilize streaming data and convert into batch data to get historical view, design such that batch data doesn’t introduce latency, utilize batch data for deeper data analysis

Design for real-time processing

Design for latency and throughput, design reference data streams, design business logic, design visualization output

Operationalize end-to-end cloud analytics solutions (30–35%)

Create a data factory

Identify data sources, identify and provision data processing infrastructure, utilize Visual Studio to design and deploy pipelines, deploy Data Factory Jobs

Orchestrate data processing activities in a data-driven workflow

Leverage data-slicing concepts, identify data dependencies and chaining multiple activities, model complex schedules based on data dependencies, provision and run data pipelines

Monitor and manage the data factory

Identify failures and root causes, create alerts for specified conditions, perform a restatement, start and stop data factory pipelines

Move, transform, and analyze data

Leverage Pig, Hive, MapReduce for data processing; copy data between on-premises and cloud; copy data between cloud data sources; leverage stored procedures; leverage Machine Learning batch execution for scoring, retraining, and update resource; extend the data factory with custom processing steps; load data into a relational store, visualize using Power BI

Design a deployment strategy for an end-to-end solution

Leverage PowerShell for deployment, automate deployment programmatically, design deployment strategies for automation

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