DP-900 Exam Question 1
What is a key advantage of choosing the serverless billing option in Azure Cosmos DB for an application that experiences intermittent traffic?
-
✓ C. Lower cost for infrequent or bursty usage
The correct option is Lower cost for infrequent or bursty usage.
Azure Cosmos DB serverless billing charges for request units consumed and for storage, so an application that has intermittent or bursty traffic only pays for the capacity it actually uses. This makes the billing model more cost effective for workloads that are idle much of the time and that only need capacity during short spikes.
Because you do not provision and pay for fixed RU per second with serverless billing, you avoid paying for idle throughput and you do not need to manage RU provisioning for unpredictable traffic patterns. That operational simplicity and the consumption based cost model are the main reasons the Lower cost for infrequent or bursty usage option is correct.
Predictable monthly billing is incorrect because serverless is consumption based and not intended to provide a fixed monthly charge. Predictable monthly billing would describe a provisioned throughput or reserved capacity model instead.
Google Cloud Spanner is incorrect because it is a different database service from another cloud provider and it is not an advantage of an Azure Cosmos DB billing option.
Automatic expansion of stored data is incorrect in this context because automatic storage growth is a general platform behavior and it is not the key billing advantage of the serverless option. The serverless choice is primarily about how you are charged for throughput and not about special storage expansion capabilities.
Cameron’s Azure Certification Exam Tip
When you see wording about infrequent, bursty, or pay per use think of consumption based pricing and identify the option that highlights lower cost for sporadic traffic.
DP-900 Exam Question 2
Fill in the blank for the Contoso cloud scenario. A(n) [?] helps organizations get the most value from their data assets. They design and build scalable data models, clean and transform datasets, and enable advanced analytics through dashboards and visual reports. A(n) [?] converts raw data into actionable findings based on the business needs identified to produce useful insights?
The correct answer is Cloud Data Analyst.
A Cloud Data Analyst is the role that designs and builds scalable data models and that cleans and transforms datasets to make them usable. A Cloud Data Analyst enables advanced analytics by producing dashboards and visual reports and converts raw data into actionable findings that align with business needs.
Cloud Database Administrator is incorrect because that role focuses on provisioning configuring and maintaining database systems and not on producing analytics or business reports.
BigQuery Data Analyst is incorrect because it refers to a specialization tied specifically to BigQuery and the question describes a general analyst role that spans designing models cleaning data and delivering insights across tools.
Cloud Data Engineer is incorrect because data engineers primarily build and operate data pipelines and infrastructure to move and prepare data rather than focusing on interpreting the data and producing dashboards and business findings.
Cameron’s Azure Certification Exam Tip
Read the role descriptions and match the primary responsibilities to the task. Focus on words like design transform and visualize to identify analyst roles and watch for options that emphasize operations or infrastructure instead.
DP-900 Exam Question 3
Which sign in method should be used to require multi factor authentication for users who connect to an Azure SQL database?
-
✓ D. Microsoft Entra authentication
Microsoft Entra authentication is correct. Microsoft Entra authentication integrates Azure SQL with the Microsoft Entra ID identity platform so that user sign ins are processed by the identity provider and can be protected with multi factor authentication.
Microsoft Entra authentication works by using Azure AD based identities and Conditional Access policies to enforce requirements such as MFA for interactive user sign ins to Azure SQL. Configuring Azure SQL to use Entra authentication means the database trusts the identity platform and therefore honors MFA rules you apply there.
Microsoft Entra authentication is the current name for the identity service formerly referred to as Azure Active Directory, and exam questions may use either name depending on when the content was written.
Certificate based authentication is incorrect because Azure SQL does not rely on client certificates as the mechanism to require per user multi factor authentication for interactive user sign ins.
Service principal authentication is incorrect because service principals represent applications or non interactive identities and they are not subject to user MFA requirements which apply to human sign ins.
SQL authentication is incorrect because SQL authentication uses static database credentials and cannot enforce Microsoft Entra based MFA for user connections.
Cameron’s Azure Certification Exam Tip
When a question asks about enforcing MFA for users think about the identity provider and features like Conditional Access rather than authentication methods that rely on static credentials.
DP-900 Exam Question 4
Identify the missing term in this sentence about Microsoft Azure Tables service where the service uses [?] to group related entities by a shared key so that entities with the same key are stored together and this technique also helps organize data and enhance scalability and performance?
Partitioning is correct. Azure Tables uses a PartitionKey to group related entities by a shared key so entities with the same key are stored together and this partitioning helps organize data and improve scalability and query performance.
Partitioning is the general term for dividing table data into segments so each partition can be stored and served efficiently across nodes. In Azure Tables the PartitionKey defines the partition and it works with the RowKey to provide unique identities and fast lookups within a partition.
Cloud Bigtable is a Google Cloud managed NoSQL wide column database and it is not the term used by Azure Tables. It is a product name and not the concept of grouping entities by key in Azure Table storage.
Sharding is a related concept that refers to distributing data across multiple shards or database instances and people sometimes use it interchangeably with partitioning. The Azure Tables service specifically uses the term partitioning and PartitionKey so sharding is not the precise answer here.
Inner joining is a relational database operation that combines rows from two tables based on matching columns and it does not describe grouping entities by a shared partition key in Azure Tables.
Cameron’s Azure Certification Exam Tip
When a question mentions grouping by a shared key or a PartitionKey look for the term partitioning or the storage specific feature because product names and similar concepts are common distractors.
DP-900 Exam Question 5
A data team at Meridian Systems uses a table based storage service for large scale key value records and they need to know the upper storage limit for a single account. What is the maximum amount of data Meridian Systems can store in one table storage account?
5 PB is correct. This value is the published maximum capacity for a single table storage account and it represents the upper storage limit you can place in one account for table service data.
The table service is part of a storage account and the account level capacity limit therefore governs how much table data you can store. The documented account limit is 5 PB and that is why this numeric option is the correct choice for the upper bound on table storage.
500 TB is incorrect because it is a lower figure than the actual account capacity limit and does not match the documented maximum for a table storage account.
Cloud Bigtable is incorrect because it is the name of a managed NoSQL database service and not a numeric storage limit. It is also a different vendor service and therefore it does not answer the question about an account capacity value.
Unlimited is incorrect because storage accounts have documented capacity limits and they are not unlimited. The correct limit is a finite maximum which is the 5 PB value given above.
Cameron’s Azure Certification Exam Tip
When a question asks about capacity limits verify the exact service scope and consult the provider documentation and remember that storage accounts commonly have a fixed per account capacity such as 5 PB.
DP-900 Exam Question 6
Which term best completes this description for a cloud operations dashboard at Fabrikam Cloud where metrics are extracted from the underlying IT systems as numbers statistics and activity counts then processed by specialized software and presented on the dashboard?
Data visualization is the correct answer.
Data visualization describes the presentation of numeric metrics and activity counts as charts, graphs, or dashboard panels so that operators can quickly see system health and trends. The question specifically mentions extracting metrics, processing them with specialized software, and presenting them on a dashboard which matches the idea of visualization.
Data querying refers to retrieving or filtering data from databases or APIs and it does not specifically imply converting metrics into visual charts for display on a dashboard.
Cloud Monitoring is a specific Google Cloud service for collecting metrics and providing dashboards and alerts, but the question asks for the general term that describes presenting processed metrics rather than the product name.
Data analytics focuses on examining data to find patterns and derive insights and it is broader than simply showing numbers as visual elements on a dashboard. Analytics may feed visualizations but it is not the act of presenting them.
Data transformation covers cleaning, reshaping, and converting data formats and it describes processing steps rather than the graphical presentation of results.
Cameron’s Azure Certification Exam Tip
When a question mentions charts, dashboards, or graphical presentation choose the term that emphasizes visualization rather than collection, transformation, or analysis.
DP-900 Exam Question 7
In the Microsoft Azure context which term fills the blank in this sentence? [?] grants temporary permissions to items inside an Azure storage account so that applications can access blobs and files without first being authenticated and it should only be used for content you intend to expose publicly?
-
✓ C. Shared Access Signature
The correct answer is Shared Access Signature.
A Shared Access Signature grants temporary and scoped permissions to specific resources inside an Azure storage account so applications can access blobs and files without requiring full authentication. A Shared Access Signature token can restrict operations and include start and expiry times so it is intended for content you choose to expose publicly for a limited period.
SAML is an authentication and federation protocol used for single sign on and identity exchange. It does not provide object level, time limited access tokens for Azure Storage so it is not the correct choice.
Cloud Storage Signed URL is a Google Cloud term for signed URLs that grant temporary access to storage objects. It is similar in function but it is not the Azure term, so it is incorrect in the Microsoft Azure context.
SSL refers to encrypting data in transit and securing connections. It does not grant permissions to storage resources or create temporary access tokens, so it does not match the description.
Cameron’s Azure Certification Exam Tip
When a question mentions temporary or scoped access to Azure storage think of Shared Access Signature and watch for platform specific phrases like Cloud Storage Signed URL that indicate another cloud provider.
DP-900 Exam Question 8
Contoso Cloud offers a platform that hosts enterprise applications and IT infrastructure for many large organizations and it includes services for both transactional and analytical data workloads. Which service delivers a fully managed relational database with near one hundred percent feature compatibility with Microsoft SQL Server?
-
✓ D. Azure SQL Managed Instance
The correct answer is Azure SQL Managed Instance.
Azure SQL Managed Instance is a platform as a service offering that provides a fully managed relational database engine with near one hundred percent feature compatibility with Microsoft SQL Server. It supports instance scoped features such as SQL Agent, cross database transactions, and other capabilities that make lift and shift migrations from on premise SQL Server straightforward while Azure handles patching, backups, high availability, and maintenance.
Managed Instance is designed for enterprise applications that require full SQL Server feature parity and minimal changes to existing databases and administrative processes when moving to Azure.
Azure SQL Database is a managed database service but it focuses on single databases and elastic pools and it does not provide full instance level compatibility with SQL Server. Some server level features and instance scoped functionality are not available in that model.
SQL Server on Azure Virtual Machines gives you full compatibility because it runs the full SQL Server stack on an IaaS VM, but it is not a fully managed PaaS offering. You must manage the operating system, patching, and many administrative tasks yourself.
Azure Synapse Analytics is an analytics and data warehousing service optimized for large scale analytical workloads and integrated analytics. It is not intended to be a fully managed OLTP relational database with near one hundred percent SQL Server feature compatibility.
Cameron’s Azure Certification Exam Tip
When a question asks for a fully managed service with near 100% SQL Server compatibility pick the Azure SQL option that explicitly advertises instance level parity and lift and shift migration support.
DP-900 Exam Question 9
Which cloud service supports interactive data exploration visualization and collaborative report creation?
The correct answer is Power BI.
Power BI is a managed Microsoft service built for interactive data exploration and visualization and it supports collaborative report creation through the Power BI service where users can share workspaces, publish apps, and comment on reports. The platform includes authoring tools and cloud hosting so teams can iterate on visuals and dashboards together and share insights with stakeholders.
Azure HDInsight is a managed big data platform for Hadoop, Spark, and Kafka and it is focused on processing and running analytical workloads rather than providing an integrated interactive visualization and collaborative reporting environment.
Azure Data Factory is a data integration and orchestration service used to build ETL and ELT pipelines and it does not offer end user visualization or collaborative report authoring capabilities.
Azure Analysis Services provides enterprise semantic models and analytical processing similar to SQL Server Analysis Services and it is used to model and serve data to reporting tools but it does not itself provide the interactive visualization and collaborative report creation features of Power BI.
Cameron’s Azure Certification Exam Tip
When a question mentions both interactive visualization and collaborative report creation choose a managed BI and reporting service such as Power BI rather than a data processing or orchestration service.
DP-900 Exam Question 10
A cloud team at NebulaApps manages a Cosmos DB account named StoreAccount42 that uses two primary keys for administration and data access and one of the primary keys was unintentionally shown in a public screencast with no evidence of misuse so far What immediate action should you take?
-
✓ C. Switch applications to the secondary primary key and then regenerate the exposed primary key
The correct answer is to Switch applications to the secondary primary key and then regenerate the exposed primary key.
You should first move application usage to the other primary key so that client connections remain functional and there is no service interruption. After the applications are using the secondary key you can safely regenerate the exposed primary key which immediately invalidates the leaked credential.
Azure Cosmos DB supports two primary keys so you can perform a seamless key rotation. This approach lets you revoke the compromised key right away while preserving availability by using the alternate key during the rotation.
Regenerate the exposed primary key only is not the best immediate action because regenerating the exposed key while applications still use it will cause outages. You should ensure clients switch to the alternate key before invalidating a key.
Create a fresh Cosmos DB account and move all data to it is an overly disruptive and time consuming step for an incident where a key has been exposed but there is no evidence of misuse. Key rotation is faster and avoids the complexity of data migration.
Apply Azure role based access control to grant minimal permissions instead of using primary keys is a good long term security improvement but it is not the quickest remediation for an immediately exposed primary key. Implementing RBAC can take planning and configuration while key rotation provides an immediate revocation of the leaked secret.
Cameron’s Azure Certification Exam Tip
When a secret is exposed act quickly to rotate the credential while keeping services available. Use the two key pattern to switch clients to the alternate key before regenerating the compromised key.
DP-900 Exam Question 11
A data engineering team at Meridian Analytics needs a storage account configuration that allows them to apply access controls at the folder level and to perform directory operations atomically, which setting should they enable?
-
✓ C. Enable hierarchical namespace on the storage account
Enable hierarchical namespace on the storage account is correct.
Enable hierarchical namespace on the storage account turns on Azure Data Lake Storage Gen2 features which include POSIX style access control lists on directories and files and support for atomic directory and file operations. This feature therefore allows the team to apply access controls at the folder level and to perform directory operations atomically as the question requires.
Enable role based access control at the account level is incorrect because RBAC governs management and resource level permissions and does not provide POSIX style folder level ACLs or atomic directory semantics that are required for directory operations.
Configure replication to read access geo redundant storage RA GRS is incorrect because replication settings control data durability and cross region read access and they do not enable folder level access controls or atomic directory operations.
Change the storage account type to BlobStorage is incorrect because the BlobStorage account kind by itself does not enable the hierarchical namespace features needed for folder ACLs and atomic directory operations. ADLS Gen2 functionality requires the hierarchical namespace capability on a compatible storage account.
Cameron’s Azure Certification Exam Tip
When a question asks for folder level ACLs and atomic directory operations look for hierarchical namespace or Azure Data Lake Storage Gen2 in the options.
DP-900 Exam Question 12
Fill in the blank in this sentence for NimbusCloud analytics tools. [?] helps you quickly detect patterns anomalies and operational problems and it lets you focus on the meaning of information rather than inspecting raw records?
Data visualization is correct because it helps you quickly detect patterns anomalies and operational problems and it lets you focus on the meaning of information rather than inspecting raw records.
Data visualization uses charts graphs and dashboards to reveal trends clusters outliers and relationships in data so that teams can understand issues at a glance and act faster. Visualizations abstract raw records into visual forms which makes it easier to spot anomalies and operational problems without reading individual rows.
BigQuery is a scalable data warehouse for storing and querying large datasets and it is not itself a visualization tool even though it can feed data to visualization systems.
Data reconciliation is the process of comparing and correcting data between sources and it focuses on data quality rather than on presenting patterns visually.
Data forecasting creates models to predict future values and trends and it is about generating predictions rather than helping users visually inspect current records for patterns or operational issues.
Cameron’s Azure Certification Exam Tip
When a question highlights seeing patterns or focusing on the meaning look for answers about visualization or dashboards rather than storage or modeling tools.
DP-900 Exam Question 13
A boutique analytics firm named Harbor Insights records click events in an Azure SQL table called WebHits with columns VisitorID, URLPath and EventTime. You need to determine which page paths receive the most visits. Which T SQL statement will return each page and the number of hits it received?
-
✓ D. SELECT URLPath, COUNT(*) AS PageViews FROM WebHits GROUP BY URLPath
SELECT URLPath, COUNT() AS PageViews FROM WebHits GROUP BY URLPath* is correct.
This statement groups the rows by URLPath and uses COUNT(*) to count every hit for each path. That returns each page path together with the total number of page views which is exactly what is needed to determine which pages receive the most visits.
SELECT URLPath, COUNT(DISTINCT VisitorID) AS UniqueVisitors FROM WebHits GROUP BY URLPath is incorrect because it counts unique visitors per path rather than total hits. That produces a count of distinct users and not the number of page views.
SELECT DISTINCT URLPath FROM WebHits is incorrect because it only lists the unique URL paths and does not provide any counts. There is no aggregation so you cannot tell which pages have more visits from that result.
SELECT URLPath FROM WebHits WHERE EventTime = (SELECT MAX(EventTime) FROM WebHits) is incorrect because it returns only rows that match the latest event time. That yields recent events only and does not aggregate or count visits across all events.
Cameron’s Azure Certification Exam Tip
When a question asks for the number of visits per category use COUNT(*) together with GROUP BY unless the question specifically asks for unique users or distinct counts.
DP-900 Exam Question 14
A small analytics team at NovaData needs to provision a NoSQL database just once for a proof of concept. Which provisioning method is most appropriate for a single one off creation of the database?
The correct option is Azure Portal.
Using the Azure Portal is the simplest approach for a single one off proof of concept because it gives an interactive graphical interface that lets the team create a NoSQL database quickly without writing infrastructure code.
The portal provides guided forms, sensible defaults, and quickstart workflows so the team can focus on testing data and queries rather than building and validating deployment scripts.
Bicep script is an infrastructure as code tool intended for repeatable, automated deployments and pipeline integration. It requires authoring templates and using tooling which is unnecessary overhead for a one time manual proof of concept unless you specifically need the deployment described as code.
ARM template deployment is a declarative, repeatable deployment method that also requires template development and validation. It is better suited for consistent, version controlled deployments rather than a single quick creation for a proof of concept.
Google Cloud Console is the management interface for Google Cloud Platform and not for Azure. It cannot be used to provision Azure NoSQL databases so it is not applicable to this scenario.
Cameron’s Azure Certification Exam Tip
When the scenario describes a single, one off resource creation choose the interactive portal unless the question explicitly asks for automation, repeatability, or infrastructure as code.
DP-900 Exam Question 15
Within the context of the Contoso Cloud platform which term completes the sentence “general purpose file storage for binary large objects fits any scenario”?
The correct answer is Azure Blob Storage.
It is the object storage service designed for large amounts of unstructured data and for binary large objects. It supports scenarios such as serving images and videos, backups and archives, and analytics data lakes, which makes it the natural fit for the phrase general purpose file storage for binary large objects.
Azure File Storage provides managed file shares accessible over SMB or NFS and is intended for lift and shift scenarios and applications that require file system semantics. It is not an object store optimized for large unstructured blobs.
Azure Disk Storage is block storage that attaches to virtual machines for persistent OS and data disks. It is designed for VM workloads and not for general purpose blob storage.
Azure Queue Storage is a messaging service used to decouple application components and to pass messages between services. It is not meant for storing binary large objects.
Cameron’s Azure Certification Exam Tip
When a question mentions binary large objects or unstructured data prefer the object storage service. Scan options for words like file share or attached disk to rule out other storage types.
DP-900 Exam Question 16
A data engineering group at Meridian Insights needs to process and query very large graph datasets to uncover relationships and perform complex traversals. Which Azure service is best suited for that type of workload?
-
✓ B. Azure Cosmos DB Gremlin API
Azure Cosmos DB Gremlin API is the correct option for processing and querying very large graph datasets to uncover relationships and perform complex traversals.
The Azure Cosmos DB Gremlin API is a managed graph database that implements the Apache TinkerPop Gremlin traversal language and it is designed for expressing complex multi hop traversals and relationship queries. It provides horizontal scale and low latency reads which helps when working with very large graphs, and it integrates with Cosmos DB features for global distribution and flexible consistency models to support large scale graph workloads.
Azure Databricks offers powerful distributed processing and can analyze graph data with additional libraries, but it is not a native graph database and it is less suited for interactive low latency graph traversals than a Gremlin API optimized store.
Azure Synapse Analytics is focused on distributed analytics and data warehousing and it can process large datasets with SQL and Spark, but it is not specialized for relationship centric traversals the way a Gremlin based graph service is.
Azure SQL Database is a relational engine that can represent relationships with tables and joins, but it lacks native Gremlin traversal support and it does not provide the traversal performance characteristics of a purpose built graph database.
Cameron’s Azure Certification Exam Tip
When a question emphasizes multi hop traversals and relationship queries look for services that natively support a graph model and the Gremlin traversal language rather than general analytics or relational engines.
DP-900 Exam Question 17
A data engineer at NorthStar Analytics must complete a description of a column oriented storage format. A(n) [?] is a columnar data format. It was developed by DataForge and SocialStream. A [?] file holds row groups. Data for each column is stored together within each row group. Each row group contains one or more chunks of data. A [?] file contains metadata that describes the row ranges inside each chunk. How should the blank be filled?
The correct answer is Parquet.
Parquet is a columnar storage file format that organizes data into row groups and stores all values for each column together within each row group. Each row group contains one or more column chunks and those chunks are further divided into pages. A Parquet file also includes footer metadata that describes the row ranges and page indexes so readers can skip data and read only the needed columns and row ranges.
ORC is also a columnar format but it uses a stripe based layout and different metadata structures so the specific description in the question matches Parquet more closely.
CSV is a plain text, row oriented format with no column chunks or file footer metadata describing row ranges.
BigQuery is a managed analytics service and not a file format that contains row groups and column chunks.
JSON is a text serialization format for structured objects and it is not a columnar storage format with row groups.
Avro is a row oriented binary serialization format that includes schemas but it stores records row by row rather than by column chunks.
XLSX is a spreadsheet file format based on XML in a zipped container and it does not use Parquet style row groups and column chunks for efficient analytic reads.
DP-900 Exam Question 18
At Contoso Data Pipelines which activities are classified as part of the orchestration control flow rather than the data transformation layer?
-
✓ C. If condition activity
The correct answer is If condition activity.
If condition activity is an orchestration control flow construct because it evaluates a boolean expression and controls whether subsequent activities run. It is used for branching and conditional execution and it orchestrates the pipeline rather than performing data movement or transformation.
Cloud Dataflow is a managed service for stream and batch data processing and it belongs to the data transformation layer because it runs data processing jobs and transforms data rather than directing pipeline control flow.
Mapping data flow is a visual, scalable data transformation feature and it executes transformations on incoming data streams or batches. It is part of the transformation layer and not a control flow activity, so it does not orchestrate branching or conditional execution.
Copy activity is designed to move or ingest data between stores and it is a data movement or transformation task rather than an orchestration construct. It does not control pipeline branching or sequencing by itself.
Cameron’s Azure Certification Exam Tip
When deciding between orchestration and transformation ask whether the activity controls execution such as branching or looping or whether it moves or transforms data. Activities that control flow are orchestration and activities that process or transfer data belong to the transformation layer.
DP-900 Exam Question 19
A payments company named HarborPay runs a real time transaction system that handles many concurrent writes and reads and requires very low latency and high throughput. Which Azure SQL Database service tier best fits this workload?
The correct option is Business Critical.
The Business Critical tier is optimized for transactional workloads that need very low latency and high throughput. It places database files on local SSD storage and uses an Always On availability architecture with multiple replicas which reduces I/O latency and improves write and read performance for high concurrency systems.
The Business Critical tier also provides readable secondary replicas that can be used to offload read workloads and it supports high compute and in-memory options that help sustain many concurrent writes and reads while keeping response times low.
General Purpose is not ideal because it uses remote storage and is designed for balanced, cost efficient workloads that tolerate some additional I/O latency compared with local SSD storage.
Hyperscale is designed mainly for very large databases and fast scale of storage and backup operations. It is not primarily chosen for the lowest possible I/O latency for heavy, concurrent OLTP workloads.
Basic is intended for small development or light production workloads and it lacks the I/O throughput and concurrency capacity required for a real time, high throughput transaction system.
Cameron’s Azure Certification Exam Tip
Look for keywords like low latency and high throughput and favor tiers that use local SSD storage and availability replicas when you need high OLTP performance.
DP-900 Exam Question 20
A mobile game studio must capture live telemetry from players to make immediate adjustments and monitor events as they happen. Which class of workload best fits this scenario?
The correct option is Streaming.
Streaming is the right workload class because live telemetry requires continuous ingestion and low latency processing so events can be analyzed and acted on as they occur. Streaming supports windowing and incremental aggregation which lets the studio adjust game parameters and monitor events in near real time.
To build a streaming pipeline you typically pair a messaging ingress with a stream processing engine. For example you might ingest events with Cloud Pub/Sub and process them with a streaming engine such as Dataflow or another stream processor to compute metrics and trigger immediate actions.
Online transaction processing (OLTP) is wrong because OLTP describes transactional database workloads that focus on many small, ACID protected reads and writes rather than continuous event stream analysis. OLTP systems are not designed for the low latency event aggregation and real time analytics required for live telemetry.
Cloud Pub/Sub is wrong as the answer because it is a messaging service and not a class of workload. It can be part of a streaming solution by handling event ingress but the workload class that describes continuous, low latency processing is streaming.
Batch processing is wrong because batch jobs collect and process data in large groups at scheduled intervals which introduces latency. This makes batch unsuitable when you need immediate adjustments and real time monitoring of player telemetry.
Cameron’s Azure Certification Exam Tip
When a question asks about continuous low latency analytics think streaming and distinguish services that enable streaming from the workload class itself.
DP-900 Exam Question 21
Which database model matches these properties All data is arranged in tables and entities are represented by tables with each record stored as a row and each attribute stored as a column All rows in the same table share the same set of columns Tables can contain any number of rows A primary key uniquely identifies each record so that no two rows share the same primary key and a foreign key refers to rows in a different related table which model fits this description?
The correct answer is Relational database.
A Relational database arranges data in tables where each entity type is a table and each record is a row while each attribute is a column. The description of a primary key uniquely identifying each record and a foreign key referring to rows in a different related table matches the fundamental relational model and how SQL databases enforce relationships and integrity.
Cloud Bigtable is not correct because it is a wide column NoSQL store that uses sparse, schema flexible tables and does not enforce fixed columns or relational foreign key constraints. It is optimized for large scale and throughput rather than relational joins and referential integrity.
NoSQL database is not correct because that is a broad category that includes many nonrelational models. The properties in the question describe a fixed tabular schema with primary and foreign keys which is specific to relational systems and not a general characteristic of NoSQL databases.
Firebase Realtime Database is not correct because it stores data as a JSON tree for real time synchronization and does not use the table row and column structure nor enforce primary key and foreign key relationships in the way relational databases do.
DP-900 Exam Question 22
Your team manages an Acme document database that exposes a MongoDB compatible interface and you need to retrieve every record that contains a particular key. Which MongoDB query operator should you use?
$exists is correct because it is the MongoDB query operator that checks for the presence of a field and so it is used to retrieve every record that contains a particular key.
The $exists operator matches documents where the specified field is present when set to true and it can be combined with other criteria to narrow results based on both presence and values.
$in is incorrect because it tests whether a field’s value equals any value in a given array and it does not check whether the key itself exists.
Google Firestore is incorrect because it names a different document database product and it is not a MongoDB query operator that you would use to test for a key in a MongoDB compatible interface.
$type is incorrect because it matches documents by the BSON type of a field and it does not solely test for the existence of the field.
Cameron’s Azure Certification Exam Tip
When a question asks about whether a field is present look for operators that test for presence rather than value or type and remember that $exists checks presence while $in and $type do not.
DP-900 Exam Question 23
Which description most accurately defines a stored procedure in a relational database management system?
-
✓ C. A collection of one or more SQL statements stored on the database server and invoked with parameters
The correct answer is A collection of one or more SQL statements stored on the database server and invoked with parameters.
A collection of one or more SQL statements stored on the database server and invoked with parameters describes a stored procedure because it bundles SQL and procedural logic on the database side and it is executed by the database engine when called. A stored procedure can accept input and output parameters and it runs under the database process so it can manage transactions, enforce permissions, and reduce client server round trips.
A serverless function executed in Cloud Functions is incorrect because Cloud Functions are external serverless compute resources and not SQL routines stored inside the database. They run in a cloud runtime and are managed separately from the database engine.
A virtual table whose rows are produced by a query is incorrect because that definition matches a view rather than a stored procedure. A view represents query results as a virtual table and it does not encapsulate procedural logic or accept invocation parameters in the same way.
A schema object that stores all of the data in the database is incorrect because tables and storage structures hold data and a schema is a namespace. No single schema object stores all data, and that description does not match the purpose of a stored procedure.
Cameron’s Azure Certification Exam Tip
Read the wording carefully and look for phrases like stored on the database server and invoked with parameters as those clues usually point to a stored procedure.
DP-900 Exam Question 24
A retail analytics team requires an analytical store that presents processed structured data for reporting and supports queries over both near real time streams and archived cold data. These analytical stores are part of the serving layer. Which service is implemented on Apache Spark and is available across multiple cloud providers?
The correct answer is Databricks.
Databricks is built on Apache Spark and is provided as a managed analytics platform across multiple cloud providers. It supports unified batch and streaming processing and integrates with Delta Lake and other components so analysts can run queries over near real time streams and archived cold data as part of a serving layer.
Google Cloud Dataproc is a managed Spark and Hadoop service but it is specific to Google Cloud and not a multi cloud Spark platform, so it does not match the requirement for availability across multiple cloud providers.
Azure Data Factory is primarily an orchestration and ETL service for moving and transforming data and it is not itself an analytical store implemented on Apache Spark for serving layer queries.
Azure Synapse Analytics is a comprehensive analytics service on Azure that can include Spark workloads, but it is an Azure native offering rather than a multi cloud managed Spark platform like the correct answer.
Cameron’s Azure Certification Exam Tip
When the exam asks for a Spark based solution that runs across clouds look for products that explicitly advertise multi cloud support and Apache Spark implementation rather than services that are tied to a single cloud provider.
DP-900 Exam Question 25
How do “structured” and “semi-structured” data differ in their organization and validation for a cloud analytics team at Nimbus Analytics?
-
✓ B. Structured data requires a predefined schema while semi-structured data contains self-describing fields and flexible structure
Structured data requires a predefined schema while semi-structured data contains self-describing fields and flexible structure.
This option is correct because structured data is defined by a fixed schema that is enforced when data is written and validated by systems like relational databases and data warehouses. Semi-structured data uses self describing formats such as JSON or XML so records can vary and the schema can be applied when the data is read.
Semi structured formats carry field names or tags with the data which makes them flexible for evolving sources and for ingesting varied records without changing a central schema. This flexibility is why teams often use schema on read for semi structured data and schema on write for structured data.
Structured data is stored exclusively in relational databases and semi-structured data is only saved in object storage is incorrect because storage is not exclusive to a format. Structured data can live in warehouses, columnar stores, or specialized databases and semi structured data can be stored in databases, document stores, or object storage depending on access and tooling.
Semi-structured data is easier to scale for large analytics workloads compared with structured data is incorrect because scalability depends on the chosen storage and processing architecture rather than the inherent format. Both structured and semi structured data can scale well when used with distributed systems and the right data engineering patterns.
Structured data is always more secure than semi-structured data is incorrect because security is determined by access controls, encryption, and governance policies rather than the data format. Both formats can be protected to strong security standards when proper controls are applied.
Cameron’s Azure Certification Exam Tip
When you see choices about schemas focus on whether the schema is enforced at write time or whether the data is self describing and validated at read time. Remember that schema on write maps to structured data and schema on read maps to semi structured data.
DP-900 Exam Question 26
How many databases can you provision inside a single Azure Cosmos account at present?
The correct option is No upper limit.
No upper limit is correct because Azure Cosmos DB does not impose a fixed maximum number of datab
