Azure Exam Question 1

Apply an IP address allow list using an IP firewall policy is correct because Cosmos DB provides account level IP firewall rules that let you permit only specified IPv4 addresses or ranges to reach the account over the public internet.

An IP firewall policy is configured at the Cosmos DB account level and can be managed from the Azure portal, Azure CLI, PowerShell, or ARM templates. This feature enforces network level access based on source IP addresses and is the appropriate mechanism when the requirement is to restrict which external IP addresses can connect to the public endpoint.

Disable public network connectivity for the Cosmos DB account is incorrect because turning off public network connectivity blocks all public access and does not provide a way to selectively allow certain external IP addresses. That option is used when you want access only via private endpoints.

Rotate the account primary keys to invalidate existing credentials is incorrect because key rotation affects authentication credentials and does not control network level access or which IP addresses can reach the service.

Use a managed network firewall to inspect and block incoming traffic is incorrect because a managed network firewall such as Azure Firewall inspects and filters traffic for virtual networks or routed traffic and is not the mechanism for applying an allow list directly to a Cosmos DB public endpoint. To restrict direct public access by source IP you would use the Cosmos DB IP firewall or move access to private endpoints.

Cameron’s DP-900 Certification Exam Tip

When a question asks about limiting which external IP addresses can reach a public endpoint look for an IP allow list or firewall rule answer rather than key rotation or disabling public access entirely.

Azure Exam Question 2

The correct option is Azure Anomaly Detector.

The service applies machine learning specifically to time series data to identify unusual activity and repeating patterns. It offers a ready made REST API and SDKs for real time and batch anomaly detection which makes it a direct fit for the retail analytics team requirements.

Azure Stream Analytics is a real time stream processing and query service for event data and it is not an out of the box machine learning anomaly detection API. It can integrate with external models but it does not itself provide the turnkey time series anomaly detection capability.

Azure Machine Learning is a comprehensive platform for building training and deploying custom machine learning models and it requires you to design and manage models rather than giving a simple, dedicated time series anomaly detection endpoint.

Google Cloud Monitoring is a monitoring and observability service for Google Cloud resources and applications and it is not the Azure offering. It focuses on metrics, logs, and alerts and is therefore not the Azure service that directly provides the described anomaly detection feature.

None of the listed services are deprecated or retired so the current Azure naming and capabilities apply.

Cameron’s DP-900 Certification Exam Tip

When a question asks about detecting anomalies in temporal data look for services that explicitly mention time series and anomaly detection or that advertise a dedicated anomaly detection API.

Azure Exam Question 3

Server admin login of the logical server is the account that will always be able to connect to the database after provisioning.

Server admin login of the logical server is created when you provision the logical server and it acts as the server level principal with permissions to connect to and manage databases on that server. This login is the persistent administrative SQL authentication identity that you can use to access any database hosted by the logical server unless you explicitly remove or disable it.

Azure Active Directory administrator account for the server is not guaranteed to exist by default because an Azure Active Directory administrator must be explicitly configured at the server level. If it is not configured then that account will not be available to connect.

Microsoft Entra ID account that created the database does not automatically receive server admin rights. The creator of a database is not automatically granted the logical server administrator role unless that account was separately configured as the server admin or given appropriate permissions.

The traditional sa SQL account is not provided for Azure SQL Database. Microsoft does not expose a built in ‘sa’ account for customer use on the managed platform.

Cameron’s DP-900 Certification Exam Tip

When a question asks which login always has access think about the server level principal. The server admin created during provisioning is the reliable answer unless the exam states an explicit change.

Azure Exam Question 4

The correct answer is Data privacy concerns the proper and lawful handling of personal information while data confidentiality concerns preventing unauthorized access to any sensitive information.

Data privacy focuses on the rights of individuals and on the rules that govern how personal information is collected, used, shared, retained and deleted. It emphasizes legal requirements such as consent, purpose limitation and data subject rights and it requires governance processes, data inventories, contractual controls and policies to meet those obligations.

Data confidentiality is a security property that aims to prevent unauthorized access to information through technical and operational controls. Typical controls include encryption, access control, least privilege, network segmentation and key management, and these controls apply to any sensitive information whether it is personal data or not.

Privacy and confidentiality overlap and confidentiality is one set of controls that supports privacy. Privacy however is broader and also requires legal and procedural measures that go beyond technical access controls.

Data privacy and data confidentiality are simply two names for the same idea is incorrect because the terms refer to related but distinct concerns. Privacy is about lawful handling and individual rights while confidentiality is about preventing unauthorized access.

Data privacy is a compliance responsibility for the customer while data confidentiality is a technical duty for the cloud vendor is incorrect because responsibilities are shared in the cloud. Customers often hold primary privacy obligations but vendors also have obligations and both parties implement confidentiality controls and safeguards.

Data privacy only applies to personally identifiable fields while data confidentiality only applies to encrypted datasets is incorrect because privacy applies to all personal data and not only to specific fields. Confidentiality is enforced by multiple controls and not only by encryption.

Cameron’s DP-900 Certification Exam Tip

When distinguishing these concepts on an exam think of privacy as the policy and legal side and think of confidentiality as the technical controls that prevent unauthorized access. Remember that confidentiality supports privacy but does not cover legal obligations on its own.

Azure Exam Question 5

The correct answer is It needs exact page layout for printing or fixed file exports.

A paginated report is designed to produce a precise, page oriented layout that is suitable for printing and for fixed file exports such as PDF. It provides control over page breaks, headers and footers, and pixel level placement which is important when receipts and inventory listings must match a printed or archival format.

The report is expected to produce about 250000 pages is not a sufficient justification on its own because large page counts do not necessarily require a paginated layout. Very large outputs may raise performance and processing concerns but they do not define the need for fixed page formatting.

It will be viewed interactively in Looker Studio is incorrect because interactive dashboards and reports are intended for exploration and interactivity. Paginated reports are optimized for static, print friendly output rather than interactive viewing in tools like Looker Studio.

It should be a visual summary with charts and interactive elements is incorrect because a visual, interactive summary is best delivered by dashboarding tools. Paginated reports do not provide the same level of interactivity and are focused on fixed layouts for printing and export.

Cameron’s DP-900 Certification Exam Tip

When you see phrases like exact page layout or print-ready on the exam favor paginated reports and choose dashboards or interactive reports when the question emphasizes charts and exploration.

Azure Exam Question 6

Provides single digit millisecond read and write latency under 8 ms at the 99th percentile worldwide is correct because it directly addresses the requirement for a globally distributed key value store that needs very low latency.

The Provides single digit millisecond read and write latency under 8 ms at the 99th percentile worldwide statement reflects the performance characteristics of the Cosmos DB Table API which is engineered for global distribution and offers multi region replication and low tail latency for reads and writes.

Service level agreement of 99.995% availability is incorrect because an availability SLA does not guarantee the single digit millisecond latency under global load and the question is specifically about latency rather than uptime.

Cloud Bigtable is incorrect because it is a Google Cloud product and not an Azure Table Storage or Cosmos DB option, so it does not answer why one would pick Cosmos DB Table API over Azure Table Storage.

SDKs available for many programming languages and platforms is incorrect because while broad SDK support is useful it is not the primary reason to choose a service when the requirement is very low, worldwide latency.

Cameron’s DP-900 Certification Exam Tip

When a question highlights very low latency worldwide look for answer choices that state explicit millisecond latency or percentile guarantees rather than general features like SDK availability or high level SLAs.

Azure Exam Question 7

The correct answer is Azure Stream Analytics.

Azure Stream Analytics is a fully managed real time analytics service that is built to ingest and process high velocity event streams with low latency. It offers a SQL like query language and built in windowing functions for event time processing and it integrates natively with Event Hubs and IoT Hub so it is the best fit for the scenario described.

Azure Databricks is a unified analytics platform based on Apache Spark and it excels at batch processing and advanced analytics. It can perform streaming with Spark Structured Streaming but it is not the serverless, purpose built service for simple low latency event stream analytics that the question asks for.

Google Cloud Dataflow is a managed stream and batch processing service on Google Cloud and it is not an Azure service, so it is not the correct choice for an Azure focused question.

Azure Synapse Analytics is an integrated analytics service that targets data warehousing and large scale analytical workloads. It can support streaming scenarios through additional components but it is primarily focused on big data analytics and not on being the dedicated real time event stream processor asked for here.

Cameron’s DP-900 Certification Exam Tip

Look for keywords like real time and event streams and choose the Azure service that is explicitly designed for low latency streaming and seamless integration with Event Hubs and IoT Hub.

Azure Exam Question 8

Avoid adding indexes for the infrequently accessed columns is correct.

Adding indexes to columns that are seldom referenced usually costs more than it helps because each index increases storage and slows down inserts updates and deletes due to extra maintenance. For a large archive table the overall query performance is best improved by indexing columns that are actually used by queries and by relying on fewer well chosen indexes rather than creating many indexes that are rarely used.

Create nonclustered indexes on every seldom used column is wrong because creating an index for every seldom used column creates heavy maintenance overhead and extra storage use and it will slow write operations without delivering meaningful read performance gains for most queries.

Create filtered indexes that target specific subsets of rows for the infrequently used columns is not the best answer for this scenario because filtered indexes can be useful for targeted query patterns but they still add maintenance and are only beneficial when a clear and frequent filter pattern exists. Creating many filtered indexes across many seldom used columns can still produce excessive overhead and complexity.

Create a clustered index on each of the rarely used columns is incorrect because a table can have only one clustered index and clustered keys should be chosen for uniqueness and frequent access patterns. It is not possible to create multiple clustered indexes and it would be inappropriate to cluster on many rarely used columns.

Cameron’s DP-900 Certification Exam Tip

When you decide whether to add an index think in terms of cost versus benefit and only add indexes that are justified by frequent query use. Use query plans and monitoring to validate any indexing change.

Azure Exam Question 9

The correct answer is Treemap visual.

Treemap visual displays data as coloured rectangles where each rectangle size represents the relative value of an item and the visual can nest rectangles to show hierarchical relationships. The layout makes it easy to compare parts of a whole and to reveal structure within categories in a compact space.

Key influencers is a statistical visual that highlights factors that affect a metric and it does not present data as sized coloured rectangles or nested hierarchy.

Filled map colors geographic regions to show spatial value distributions and it is focused on maps rather than rectangles or hierarchical nesting.

Matrix displays data in rows and columns similar to a pivot table and it does not use area sized rectangles or nested boxes to represent values and hierarchy.

Cameron’s DP-900 Certification Exam Tip

Focus on the visual description words such as rectangles, area, and nested when choosing a visual. Those keywords usually point to a Treemap visual rather than a map or tabular visual.

Azure Exam Question 10

The correct option is gcloud command line or Cloud SDK.

gcloud command line or Cloud SDK provides a cross platform command line tool that you can install on Linux macOS or Windows and you can place the gcloud commands into a script that runs from any operating system shell.

With the Cloud SDK you can automate provisioning of nonrelational databases by running a sequence of gcloud commands in bash or PowerShell and you can integrate those scripts into CI pipelines or run them interactively as needed.

Deployment Manager templates are declarative infrastructure as code artifacts described in YAML or Jinja and they are applied as templates rather than executed as a linear script of shell commands, so they do not match the question.

Cloud Console is the web based graphical interface for Google Cloud and it is designed for interactive use in a browser rather than for running a script of commands from an operating system shell.

Client libraries are language specific SDKs used by application code to call Google Cloud APIs and they require writing program code in languages like Python Java or Go rather than composing a shell script made of commands.

Cameron’s DP-900 Certification Exam Tip

Azure Exam Question 11

Cold data is accessed infrequently while hot data is accessed frequently and needs rapid availability is correct.

Hot data describes datasets that are accessed frequently and that require low latency and quick retrieval. Systems or storage classes for hot data are optimized for immediate availability and performance so applications can read and write rapidly.

Cold data is characterized by infrequent access and by tolerance for slower retrieval times which makes it suitable for lower cost storage tiers or archives. The primary differentiator is the access pattern and the required availability rather than format or location.

Hot data tends to be more expensive to retain while cold data normally costs less to store is incorrect because cost differences are a consequence of performance and access requirements rather than the defining attribute. Cost may often align with hot versus cold but it is not the core distinction.

Hot data is structured while cold data is unstructured is incorrect because whether data is structured or unstructured does not determine its temperature. Both structured and unstructured datasets can be hot or cold depending on how often they are accessed.

Hot data is stored in primary cloud storage while cold data is kept on local on premise servers is incorrect because cold data is commonly placed into lower cost cloud storage tiers or archival services rather than necessarily being kept on premise. The classification is about access frequency and availability rather than physical location.

Cameron’s DP-900 Certification Exam Tip

When answering questions about hot versus cold data focus on the frequency of access and the speed of retrieval required. Those phrases are usually the strongest clues.

Azure Exam Question 12

The correct option is Power BI Desktop.

Power BI Desktop is a desktop authoring tool that can connect to a wide variety of data sources and combine them into a unified data model for analysis and visualization.

Power BI Desktop provides built in data transformation and modeling features and a visual canvas to create interactive reports that you can publish to the Power BI service for sharing with stakeholders.

Azure Data Factory is a cloud service for building and orchestrating data pipelines and it focuses on data movement and transformation rather than interactive report authoring.

Looker Studio is Google Clouds reporting and dashboard product and it is a different vendor solution so it is not the Microsoft desktop authoring tool expected by this question.

Power BI Mobile app is intended for viewing and interacting with published reports on phones and tablets and it does not provide the authoring and data import capabilities of Power BI Desktop.

Cameron’s DP-900 Certification Exam Tip

When a question asks about importing, modeling, and authoring interactive reports pick an authoring tool such as Power BI Desktop rather than a mobile viewer or a pipeline orchestration service.

Azure Exam Question 13

Database Administrator is correct because that job title specifically names the person who operates database systems grants user access maintains backup copies of data and recovers data after failures.

A Database Administrator installs and configures database software and manages day to day operations. A Database Administrator is responsible for access control and permissions and for implementing backup and recovery procedures and for restoring data after hardware or software failures.

Data Engineer is incorrect because data engineers build and maintain data pipelines and transformation processes and they do not typically own the operational tasks of backups access control and disaster recovery.

Cloud SQL is incorrect because that name refers to a managed database service offered by Google Cloud and it is not a job title on Microsoft Azure.

Data Analyst is incorrect because data analysts focus on querying analyzing and visualizing data and they do not generally perform the operational duties of running and recovering database systems.

Cameron’s DP-900 Certification Exam Tip

When a question describes operational responsibilities such as backup access control and recovery look for a role that matches day to day system administration tasks rather than data pipeline or analytics titles.

Azure Exam Question 14

The correct answer is A preconfigured integrated hardware and software system optimized for analytics.

A data warehouse appliance refers to a tightly integrated bundle of hardware, storage, networking and database software that is tuned and validated to run analytical workloads efficiently. Vendors deliver these appliances so enterprises get predictable query performance, simplified deployment and a system that is optimized end to end for analytics rather than a collection of separate components.

Appliances are typically used where organizations want on prem or dedicated systems that reduce the work of integrating and tuning servers, storage and analytics software. The integrated design supports parallel processing, high throughput I O and storage layouts that accelerate complex reporting and aggregation queries.

A serverless managed analytics service like BigQuery is incorrect because a serverless cloud service is delivered as a managed software offering and does not include a prepackaged hardware appliance that the vendor ships or deploys to the customer environment.

A repository for storing massive raw datasets in multiple formats is incorrect because that describes a data lake, which focuses on flexible raw storage and schema on read rather than an optimized, integrated system designed for fast analytical query processing.

A tool that orchestrates ingestion and transformation workflows is incorrect because orchestration and ETL tools manage data flows and processing pipelines and do not provide the integrated hardware plus analytics software that characterizes a data warehouse appliance.

Cameron’s DP-900 Certification Exam Tip

When you see the word appliance focus on whether hardware is part of the deliverable. If the vendor provides bundled hardware and tuned software then the appliance answer is likely correct rather than a serverless or orchestration option.

Azure Exam Question 15

The correct option is You are billed for the provisioned RU/s regardless of consumption.

This is because Azure Cosmos DB charges based on the throughput you provision for a container or database. If you allocate a high number of RU/s you will incur costs for that provisioned capacity even when your workload does not consume all of those RUs. Overprovisioning therefore increases cost without guaranteed proportional gains in real world query latency or efficiency.

Provisioned RU/s do not affect billing or performance is incorrect because provisioned RU/s directly determine billing and they set the available capacity for operations which can affect performance when they are too low or unevenly distributed.

Cosmos DB will automatically decrease allocated RU/s when usage drops is incorrect because Cosmos DB does not automatically lower a manually provisioned RU/s. There is an autoscale mode that can adjust throughput within a configured range, but you still set the maximum throughput and billing is tied to the configured throughput model.

Query throughput automatically scales in line with the provisioned RU/s and improves performance proportionally is incorrect because increasing provisioned RU/s raises available capacity but query performance depends on indexing, partition key design, and the RU cost of the query. Higher RU/s does not guarantee a proportional improvement in query performance.

Cameron’s DP-900 Certification Exam Tip

When a question mentions billing focus on the word provisioned. Remember that Cosmos DB charges are tied to the throughput you configure not to the RUs you actually consume.

Azure Exam Question 16

Power BI is the correct choice for building and sharing interactive dashboards and reports.

Power BI provides real time dashboards and supports streaming datasets so operational metrics can be displayed as they happen. It integrates with Azure data sources and offers interactive visuals along with easy sharing and access control so managers can monitor performance through the Power BI service and apps.

Azure Databricks is focused on big data processing and machine learning and it is not primarily a dashboarding or reporting tool.

Azure Data Factory is an orchestration and data movement service used to build ETL and ELT pipelines and it does not provide built in interactive dashboards for end users.

Azure Synapse Analytics is a unified analytics and data warehousing platform that can store and prepare data for reporting but it is not the service used to build and share interactive dashboards.

Cameron’s DP-900 Certification Exam Tip

When a question asks about creating and sharing live dashboards choose a visualization service like Power BI rather than data processing or orchestration tools.

Azure Exam Question 17

The correct option is API selection for the account.

You must choose the account API when you create an Azure Cosmos DB account and the API selection for the account cannot be changed later for that account. The choice determines the protocol and data model such as Core (SQL), MongoDB, Cassandra, Gremlin, or Table and so if you need a different API you must create a new account and migrate your data.

Multi region writes can be enabled or disabled and configured by adding or removing regions and updating account settings after the account exists, so this option is not restricted to the initial creation.

Geo replication regions are managed through the Global Distribution feature and you can add or remove replica regions after the account is created, so region replication is not fixed at creation time.

Account provisioning tier for production or development is not a single immutable selection that is enforced only at creation. Many provisioning and throughput settings can be adjusted after creation, although very large or structural changes may require creating a new account and migrating data.

Cameron’s DP-900 Certification Exam Tip

Remember that API selection is an immutable choice for an Azure Cosmos DB account, so pick the correct API up front and practice account migration so you can handle cases where a different API is required.

Azure Exam Question 18

The correct answer is Transaction isolation level settings.

Transaction isolation level settings determine how concurrent transactions see and interact with each other and they directly control the isolation guarantee of ACID. By selecting levels such as read uncommitted read committed repeatable read or serializable the database prevents undesirable phenomena like dirty reads non repeatable reads and phantom reads and thus enforces isolation among transactions.

Transaction isolation level settings operate together with atomic commit protocols and logging to provide full ACID behavior but the isolation configuration is the database property that specifically governs how strictly transactions are isolated from one another.

Database normalization is a design technique for organizing schema to reduce redundancy and update anomalies. It does not control transactional concurrency or isolation and therefore it does not enforce ACID transaction semantics.

Transaction logging and write ahead logs are essential for durability and for recovering to a consistent state after a crash and they support atomic commits. They do not by themselves control how concurrent transactions are isolated so they are not the property that enforces isolation across transactions.

High availability and failover focus on keeping the database available during failures and on minimizing downtime. These features help with availability and continuity but they do not directly enforce the transactional ACID guarantees such as isolation.

Cameron’s DP-900 Certification Exam Tip

When asked about ACID map each option to a specific ACID property and remember that isolation is controlled by isolation level settings while durability and atomicity are implemented with logging and commit protocols.

Azure Exam Question 19

The correct answer is Azure Cosmos DB.

Azure Cosmos DB is a globally distributed, multi model database that offers guaranteed single digit millisecond read and write latency at the 99th percentile and automatic horizontal partitioning for high throughput. It also provides automatic indexing and tunable consistency levels which make it a good fit for live chat workloads that need extremely fast reads and writes together with durable storage and optional global replication.

Azure Cache for Redis provides very low latency because it is an in memory cache, but it is primarily a cache rather than a durable primary message store. Persistence options are limited and using it as the sole storage layer risks data loss or added complexity for durability and global replication.

Azure Blob Storage is object storage that is optimized for large binary objects and high throughput streaming. It is not designed for the small, low latency indexed reads and writes and the rich querying patterns that a chat message store typically requires.

Azure SQL Database is a managed relational database that supports transactions and complex queries. It can scale well for many scenarios but it does not natively provide the same predictable ultra low latency and global distribution at massive scale without additional sharding or architectural complexity.

Cameron’s DP-900 Certification Exam Tip

When a question emphasizes extremely fast reads and writes and also requires durable or globally distributed storage prefer databases that advertise single digit millisecond latency and global distribution. Confirm whether the requirement is for a cache or for a primary durable store before selecting an in memory solution like Azure Cache for Redis.

Azure Exam Question 20

Dashboard is correct.

A Dashboard in Power BI is a single page canvas that collects visuals and tiles pinned from reports and other sources and it is designed to be shared or distributed to other users as a consolidated view.

Report is incorrect because reports typically contain one or more pages for detailed exploration and they are not the single page, shareable summary described by the question.

BigQuery table is incorrect because that term refers to a Google BigQuery data storage object and it is not a Power BI artifact for packaging or sharing visuals.

Dataset is incorrect because a dataset is the underlying data model or source that reports and dashboards use and it is not a single page collection of visuals to distribute.

Cameron’s DP-900 Certification Exam Tip

Azure Exam Question 21

Support for T SQL based analytics that will be familiar to SQL Server developers is correct because Azure Synapse offers a native T SQL query surface that aligns with SQL Server conventions and tools.

Azure Synapse provides both dedicated SQL pools and serverless SQL pools that support T SQL syntax and familiar SQL Server constructs such as views and stored procedures. This reduces friction for SQL Server developers who want to run analytics without learning a different query language or execution model.

Databricks is built around Apache Spark and uses Spark SQL and other Spark APIs. While Databricks can run SQL queries it does not provide the same native T SQL compatibility and SQL Server oriented tooling that Synapse delivers.

Capability to dynamically scale compute resources to meet changing workloads is incorrect because both Synapse and Databricks support autoscaling and flexible compute provisioning, so this capability is not unique to Synapse.

Native connector integration with Google BigQuery is incorrect because integration with external systems like BigQuery is not a unique Synapse capability and both platforms can connect to external data sources through connectors or integration tools.

Ability to execute distributed parallel processing across clusters is incorrect because distributed parallel processing is a core capability of Spark and of Synapse distributed query engines, so it does not uniquely differentiate Synapse from Databricks.

Cameron’s DP-900 Certification Exam Tip

When asked which feature is unique think about native language and developer surface rather than general capabilities. Features like T SQL compatibility are often the differentiator between Synapse and Spark based platforms.

Azure Exam Question 22

Filter documents by the partition key value is correct.

Filtering by the partition key directs the query to the partition or partitions that contain the requested items so the database can avoid scanning other partitions. This approach is the most efficient way to return all products for a given item type because partitions are how Azure Cosmos DB organizes and indexes documents and targeted queries use fewer request units and complete faster.

Use the change feed to track updates in the container is incorrect because the change feed is intended to stream inserts and updates for incremental processing or eventing. It is not designed as a general purpose query mechanism to return all current documents for a particular partition key.

BigQuery is incorrect because BigQuery is a Google Cloud analytics service and it does not query documents inside an Azure Cosmos DB container directly. It is not the proper operation to fetch items from Cosmos DB.

Scan every document in the container is incorrect because scanning the full container is inefficient and will consume far more request units and time. When a partition key exists you should target that key to avoid a full container scan.

Cameron’s DP-900 Certification Exam Tip

When a container uses a partition key prefer queries that filter by that partition key to reduce RU charges and improve performance. Use point reads when you have both the id and partition key for the fastest access.

Azure Exam Question 23

The correct answer is Data stored persistently on disks, optical media, or cloud object storage.

This phrase refers to information that resides on physical storage or in cloud object stores when it is not being processed or transmitted. Examples include files on disks, database records, backups, and blobs in object storage. Protecting this stored data typically involves encryption at rest, access controls, and secure key management to prevent unauthorized access to persisted content.

Data moving across a network between systems is incorrect because that describes data in transit. Data in transit is protected with transport level controls such as TLS and secure networking rather than the controls used for data at rest.

Data held temporarily in a system’s RAM or CPU cache is incorrect because that describes data in use. Data in memory is ephemeral and is addressed with different protections like memory encryption and runtime isolation.

Data queued in a write buffer while awaiting final persistence is incorrect because write buffers are transient and part of ongoing input output operations. Queued data is considered in flight or in use until it is committed to persistent storage and is not classified as data at rest.

Cameron’s DP-900 Certification Exam Tip

Remember that data at rest means data persisted on storage while data in transit moves across networks and data in use sits in memory. Match protections to the correct state when answering questions.

Azure Exam Question 24

Batch workload for the product catalog that is loaded every 24 hours to a data warehouse, streaming workload for online purchases loaded to the warehouse as they occur, and micro batch workload for inventory updates applied after every 1,200 transactions is the correct option.

The product catalog is refreshed once every 24 hours so it matches a batch workload. Batch processing is appropriate for scheduled, bulk transfers to a data warehouse where latency can be higher and throughput is the priority.

Online purchases arrive as individual events and need to be loaded as they occur so they match a streaming workload. Streaming processing supports low latency ingestion and immediate availability of each event in the warehouse.

Inventory updates that are applied after every 1,200 transactions match a micro batch workload because the updates are collected into small frequent batches before being applied. Micro batching gives a balance between throughput and near real time responsiveness for grouped events.

Streaming workload for the product index that is refreshed every 24 hours to a data warehouse, micro batch workload for customer purchases that are pushed to the warehouse as they occur, and batch workload for inventory reconciliations that are processed after every 1,200 transactions is incorrect because the product index refreshed daily is a batch pattern rather than streaming, customer purchases that arrive as they occur need streaming rather than micro batching, and inventory reconciliations triggered every 1,200 transactions are a micro batch pattern rather than a large scheduled batch.

Micro batch workload for the product catalog that is refreshed every 24 hours to a data warehouse, batch workload for online purchases that are accumulated before loading, and streaming workload for inventory updates that are emitted after every 1,200 transactions is incorrect because a product catalog updated daily is a batch workload and not micro batch, online purchases that are accumulated before loading describe batch processing rather than streaming, and inventory updates emitted after a count of transactions are better described as micro batch rather than continuous streaming.