This is top class certification exam of Microsoft. It is designed by group of Microsoft advanced level official certification experts. It has covered all the aspects of depth in the technical questions and delivery methodology of official Microsoft certification by following the complete exam syllabus. This certification questions will give you the most realistic experience in the real world exam.
Exam Details:
This Exam contains multiple choices, multi selection and true or false questions.
Exam Time Limit: 90 Minutes
Exam Syllabus:
Administer
and Provision HDInsight Clusters
·
Create a cluster in a private virtual network, create a cluster
that has a custom metastore, create a domain-joined cluster, select an
appropriate cluster type based on workload considerations, customize a cluster
by using script actions, provision a cluster by using Portal, provision a
cluster by using Azure CLI tools, provision a cluster by using Azure Resource
Manager (ARM) templates and PowerShell, manage managed disks, configure vNet
peering
·
Provision users who have different roles; manage users, groups,
and permissions through Apache Ambari, PowerShell, and Apache Ranger; configure
Kerberos; configure service accounts; implement SSH tunneling; restrict access
to data
·
Ingest data from cloud or on-premises data; store data in Azure
Data Lake; store data in Azure Blob Storage; perform routine small writes on a
continuous basis using Azure CLI tools; ingest data in Apache Hive and Apache
Spark by using Apache Sqoop, Application Development Framework (ADF), AzCopy,
and AdlCopy; ingest data from an on-premises Hadoop cluster
·
Manage metastore upgrades; view and edit Ambari configuration
groups; view and change service configurations through Ambari; access logs
written to Azure Table storage; enable heap dumps for Hadoop services; manage
HDInsight configuration, use HDInsight .NET SDK, and PowerShell; perform
cluster-level debugging; stop and start services through Ambari; manage Ambari
alerts and metrics
·
Describe YARN architecture and operation; examine YARN jobs
through ResourceManager UI and review running applications; use YARN CLI to
kill jobs; find logs for different types of jobs; debug Hadoop and Spark jobs;
use Azure Operations Management Suite (OMS) to monitor and manage alerts, and
perform predictive actions
Implement Big Data Batch
Processing Solutions
·
Define external Hive tables; load data into a Hive table; use
partitioning and bucketing to improve Hive performance; use semi-structured
files such as XML and JSON with Hive; join tables with Hive using shuffle joins
and broadcast joins; invoke Hive UDFs with Java and Python; design scripts with
Pig; identify query bottlenecks using the Hive query graph; identify the
appropriate storage format, such as Apache Parquet, ORC, Text, and JSON
·
Share resources between Spark applications using YARN queues and
preemption, select Spark executor and driver settings for optimal performance,
use partitioning and bucketing to improve Spark performance, connect to
external Spark data sources, incorporate custom Python and Scala code in a
Spark DataSets program, identify query bottlenecks using the Spark SQL query
graph
·
Create and customize a cluster by using ADF; attach storage to a
cluster and run an ADF activity; choose between bring-your-own and on-demand
clusters; use Apache Oozie with HDInsight; choose between Oozie and ADF; share
metastore and storage accounts between a Hive cluster and a Spark cluster to
enable the same table across the cluster types; select an appropriate storage
type for a data pipeline, such as Blob storage, Azure Data Lake, and local
Hadoop Distributed File System (HDFS)
Implement Big Data Interactive
Processing Solutions
·
Execute queries using Spark SQL, cache Spark DataFrames for
iterative queries, save Spark DataFrames as Parquet files, connect BI tools to
Spark clusters, optimize join types such as broadcast versus merge joins,
manage Spark Thrift server and change the YARN resources allocation, identify
use cases for different storage types for interactive queries
·
Use Jupyter and Apache Zeppelin for visualization and developing
tidy Spark DataFrames for modeling, use Spark SQL’s two-table joins to merge
DataFrames and cache results, save tidied Spark DataFrames to performant format
for reading and analysis (Apache Parquet), manage interactive Livy sessions and
their resources
·
Enable Hive LLAP through Hive settings, manage and configure
memory allocation for Hive LLAP jobs, connect BI tools to Interactive Hive
clusters
·
Perform interactive querying and visualization, use Ambari Views,
use HiveQL, parse CSV files with Hive, use ORC versus Text for caching, use
internal and external tables in Hive, use Zeppelin to visualize data
·
Use Phoenix in HDInsight; use Phoenix Grammar for queries;
configure transactions, user-defined functions, and secondary indexes; identify
and optimize Phoenix performance; select between Hive, Spark, and Phoenix on
HBase for interactive processing; identify when to share metastore between a
Hive cluster and a Spark cluster
Implement Big Data Real-Time
Processing Solutions
·
Define DStreams and compare them to Resilient Distributed Dataset
(RDDs), start and stop streaming applications, transform DStream (flatMap,
reduceByKey, UpdateStateByKey), persist long-term data stores in HBase and SQL,
persist Long Term Data Azure Data Lake and Azure Blob Storage, stream data from
Apache Kafka or Event Hub, visualize streaming data in a PowerBI real-time
dashboard
·
Use DataFrames and DataSets APIs to create streaming DataFrames
and Datasets; create Window Operations on Event Time; define Window
Transformations for Stateful and Stateless Operations; stream Window Functions,
Reduce by Key, and Window to Summarize Streaming Data; persist Long Term Data
HBase and SQL; persist Long Term Data Azure Data Lake and Azure Blob Storage;
stream data from Kafka or Event Hub; visualize streaming data in a PowerBI
real-time dashboard
·
Create Storm clusters for real-time jobs, persist Long Term Data
HBase and SQL, persist Long Term Data Azure Data Lake and Azure Blob Storage,
stream data from Kafka or Event Hub, configure event windows in Storm,
visualize streaming data in a PowerBI real-time dashboard, define Storm
topologies and describe Storm Computation Graph Architecture, create Storm
streams and conduct streaming joins, run Storm topologies in local mode for
testing, configure Storm applications (Workers, Debug mode), conduct Stream
groupings to broadcast tuples across components, debug and monitor Storm jobs
·
Create Spark and Storm clusters in the virtual network, manage
partitions, configure MirrorMaker, start and stop services through Ambari,
manage topics
· Identify HBase use cases in HDInsight, use HBase Shell to create updates and drop HBase tables, monitor an HBase cluster, optimize the performance of an HBase cluster, identify uses cases for using Phoenix for analytics of real-time data, implement replication in HBase
How this exam is useful to you?
This exam is intended for the people who are preparing for the 70-775 Perform Data Engineering on Microsoft Azure HDInsight Certification. This exam is prepared exclusively as per the real certification exam. This will definitely help to check your skills before attempting the main exam. Also it will give the complete idea how main exam will be.
This exam will evaluate your skills and will give you report on how far you are skillful in 70-775 Perform Data Engineering on Microsoft Azure HDInsight certification exam.
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Exam Details:
This Exam contains multiple choices, multi selection and true or false questions.
Exam Time Limit: 90 Minutes
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