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Last Updated: Oct 24, 2026
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A failed Microsoft Perform Data Engineering on Microsoft Azure HDInsight attempt costs the full exam fee, weeks of waiting, and a dent in your confidence. SurePassExams's 070-775 question bank — 63 expert-verified items — exists to give your first attempt its best possible shot.
Microsoft 070-775 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement Big Data Interactive Processing Solutions | 20% | - Access data with Apache Phoenix on HBase - Query data with Spark SQL and Interactive Hive
|
| Topic 2: Administer and Provision HDInsight Clusters | 30% | - Deploy and configure HDInsight clusters
|
| Topic 3: Implement Big Data Batch Processing Solutions | 30% | - Work with Apache Spark
|
| Topic 4: Implement Big Data Real-Time Processing Solutions | 20% | - Develop Spark Streaming applications
|
Common Questions About the Microsoft 070-775 Exam
The 070-775 is the exam Microsoft uses to award the MCSA: Data Engineering with Azure certification, positioned at the Professional level of its program. It tests applied knowledge — the kind you'd use on the job — across the published Microsoft Perform Data Engineering on Microsoft Azure HDInsight objectives, which is why candidates who only memorize definitions tend to struggle. It also forms part of a broader certification track alongside 70-776 Perform Big Data Engineering on Microsoft Cloud Services, MCSE: Data Management and Analytics, so a pass here compounds in value as your career grows. Whether this is your first certification or one of many, the 070-775 exam rewards structured, question-driven preparation.
The Microsoft Perform Data Engineering on Microsoft Azure HDInsight syllabus breaks into 4 domains, led by Administer and Provision HDInsight Clusters (30%), Implement Big Data Interactive Processing Solutions (20%), and Implement Big Data Real-Time Processing Solutions (20%). Read those percentages as instructions: the heaviest domains produce the most points, so they deserve the deepest practice. The complete, domain-by-domain syllabus sits in the exam topics section above — plan your weeks around it.
The Microsoft Perform Data Engineering on Microsoft Azure HDInsight exam packs 40-60 questions into 120 minutes. Turn those figures into a plan before test day: divide the clock by the question count, memorize your per-question budget, and let any question that busts its budget get flagged and revisited later. Candidates who practice this discipline in advance — with timed mock runs in the SurePassExams test engine, for instance — spend exam day answering questions instead of managing panic.
Microsoft requires 700 / 1000 to pass the Microsoft Perform Data Engineering on Microsoft Azure HDInsight exam, and registration runs $165 USD (varies by region). That fee is charged again in full for every retake, which makes "I'll just try it and see" an expensive study strategy. The smarter sequence: drill with SurePassExams practice tests under timed conditions until your scores clear the passing bar with room to spare, then register. Confidence built on data beats confidence built on hope.
The official Microsoft Perform Data Engineering on Microsoft Azure HDInsight format list includes: Multiple choice, Drag and drop, Case studies, Build list/reorder. Each type punishes a different bad habit — scenario items punish skimming, elimination-based items punish hasty first instincts — so vary your practice accordingly. Working through SurePassExams's 63 questions exposes you to every format on that list until none of them can surprise you.
Microsoft delivers the 070-775 exam in English. Choose the language in which you read fastest and argue with answer options most precisely — under a ticking clock, that's what fluency really means. If English is your pick, every session with SurePassExams's English-language 070-775 practice questions quietly doubles as exam-vocabulary training.
The credential you earn through the Microsoft Perform Data Engineering on Microsoft Azure HDInsight exam remains valid for MCSA certification retired, no active validity. Put the expiry date in your calendar the day you pass, and research the recertification path months before it matters — renewal handled early is an afternoon task; handled late, it's a crisis. Since Microsoft sets and revises these policies, confirm the current rules on the official certification page.
The 63 expert-written questions are identical across versions — what changes is how you meet them:
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If you can't decide, start with the free PDF demo, then add a test engine when you're ready for timed practice.
We do. The free Microsoft Perform Data Engineering on Microsoft Azure HDInsight PDF demo on SurePassExams shows you real sample questions and the depth of the verified answers before you spend anything. Purchases include 365 days of free updates, and once that year ends, extending your update service costs just 50% of the regular price through your member zone. Try the demo first — an informed buyer is exactly who this material is built for.
Microsoft evolves its exams with the technology, so SurePassExams's experts review the Microsoft Perform Data Engineering on Microsoft Azure HDInsight question bank continuously — analyzing real exam feedback and syllabus shifts year after year, then revising the material to match. Your purchase includes 365 days of free updates delivered through your member zone; watch the New Releases section or the SurePassExams newsletter, and always re-check 3-4 days before your exam. Expired products can be repurchased at 50% off to restart updates.
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Start with evidence, not promises: download the free 070-775 demo, work through the sample questions, and see why so many candidates prepare for Microsoft Perform Data Engineering on Microsoft Azure HDInsight with SurePassExams's 63 practice questions.
Microsoft Perform Data Engineering on Microsoft Azure HDInsight Sample Questions:
Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.
You are implementing a batch processing solution by using Azure HDInsight.
You need to integrate Apache Sqoop data and to chain complex jobs. The data and the jobs will implement MapReduce.
What should you do?
- A. Increase the number of spark.executor.cores in an Apache Spark job that stores the data in a text format.
- B. Use a broadcast join in an Apache Hive query that stores the data in an ORC format.
- C. Decrease the level of parallelism in an Apache Spark job that stores the data in a text format.
- D. Use an Azure Data Factory linked service that stores the data in an Azure DocumentDB database.
- E. Use an action in an Apache Oozie workflow that stores the data in a text format.
- F. Increase the number of spark.executor.instances in an Apache Spark job that stores the data in a text format.
- G. Use a shuffle join in an Apache Hive query that stores the data in a JSON format.
- H. Use an Azure Data Factory linked service that stores the data in Azure Data Lake.
Correct Answer: E 🗳️
Explanation: Only visible for SurePassExams members. You can sign-up / login (it's free).
Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.
You are implementing a batch processing solution by using Azure HDInsight.
You have a table that contains sales dat
a.
You plan to implement a query that will return the number of orders by zip code.
You need to minimize the execution time of the queries and to maximize the compression level of the resulting data.
What should you do?
- A. Increase the number of spark.executor.cores in an Apache Spark job that stores the data in a text format.
- B. Use a broadcast join in an Apache Hive query that stores the data in an ORC format.
- C. Decrease the level of parallelism in an Apache Spark job that stores the data in a text format.
- D. Use an Azure Data Factory linked service that stores the data in an Azure DocumentDB database.
- E. Use an action in an Apache Oozie workflow that stores the data in a text format.
- F. Increase the number of spark.executor.instances in an Apache Spark job that stores the data in a text format.
- G. Use a shuffle join in an Apache Hive query that stores the data in a JSON format.
- H. Use an Azure Data Factory linked service that stores the data in Azure Data Lake.
Correct Answer: B 🗳️
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
You have an initial dataset that contains the crime data from major cities.
You plan to build training models from the training dat
a. You plan to automate the process of adding more data to the training models and to constantly tune the models by using the additional data, including data that is collected in near real-time. The system will be used to analyze event data gathered from many different sources, such as Internet of Things (IoT) devices, live video surveillance, and traffic activities, and to generate predictions of an increased crime risk at a particular time and place.
You have an incoming data stream from Twitter and an incoming data stream from Facebook, which are event-based only, rather than time-based. You also have a time interval stream every 10 seconds.
The data is in a key/value pair format. The value field represents a number that defines how many times a hashtag occurs within a Facebook post, or how many times a Tweet that contains a specific hashtag is retweeted.
You must use the appropriate data storage, stream analytics techniques, and Azure HDInsight cluster types for the various tasks associated to the processing pipeline.
You are planning a storage strategy for a large amount of analytic data used for the crime data analytics system. The initial data load involves over 100 billion records, and more than two billion records will be added daily.
You already created an Apache Hadoop cluster in HDInsight premium.
You need to implement the storage strategy to meet the following requirements:
The storage capacity must support 50 TB.
The storage must be optimized for Hadoop.
The data must be stored in its native format.
Enterprise-level security based on Active Directory must be supported.
What should you create?
- A. an Azure Data Lake Store account by using the Azure portal
- B. a virtual machine (VM) by using the Data Science Virtual Machine template for Windows that has premium storage, a G-series size, and uses Microsoft SQL Server 2016 to store the data
- C. an Azure Data Lake Analytics service by using Azure PowerShell
- D. an Azure Blob storage account by using the Azure portal
Correct Answer: A 🗳️
Explanation: Only visible for SurePassExams members. You can sign-up / login (it's free).
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in this series.
You are planning a big data infrastructure by using an Apache Spark cluster in Azure HDInsight. The cluster has 24 processor cores and 512 GB of memory.
The architecture of the infrastructure is shown in the exhibit. (Click the Exhibit button.)
The architecture will be used by the following users:
Support analysts who run applications that will use REST to submit Spark jobs.
Business analysts who use JDBC and ODBC client applications from a real-time view. The business analysts run monitoring queries to access aggregate results for 15 minutes. The results will be referenced by subsequent queries.
Data analysts who publish notebooks drawn from batch layer, serving layer, and speed layer queries. All of the notebooks must support native interpreters for data sources that are batch processed. The serving layer queries are written in Apache Hive and must support multiple sessions. Unique GUIDs are used across the data sources, which allow the data analysts to use Spark SQL.
The data sources in the batch layer share a common storage container. The following data sources are used:
Hive for sales data
Apache HBase for operations data
HBase for logistics data by using a single region server
The business analysts report that they experience performance issues when they run the monitoring queries.
You troubleshoot the performance issues and discover that the intermediate tables generated when the analysts run the queries cause pressure for the Java Virtual Machine (JVM) garbage collection per job.
Which configuration settings should you modify to alleviate the performance issues?
- A. spark.sql.shuffle.partitions
- B. spark.sql.broadcastTimeout
- C. spark.sql.inMemoryColumnarStorage.batchSize
- D. spark.sql.files.openCostInBytes
Correct Answer: A 🗳️
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this sections, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Apache Pig table named Sales in Apache HCatalog.
You need to make the data in the table accessible from Apache Pig.
Solution: You use the following script.
Does this meet the goal?
- A. No
- B. Yes
Correct Answer: A 🗳️
Explanation: Only visible for SurePassExams members. You can sign-up / login (it's free).
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