Question 337 of 982

Describe considerations for working with non-relational data on Azure →hardMultiple ChoiceObjective-mapped

Quick Answer

The answer is a combination of Azure Cosmos DB with a 90-day TTL and Azure Blob Storage, connected by Azure Data Factory. This design works because Cosmos DB delivers sub-second query latency on player ID and event type for real-time analytics, while its Time-to-Live feature automatically marks data for expiration after 90 days, allowing Azure Data Factory to copy that expired telemetry to Blob Storage’s Cool or Archive tiers for cost-effective long-term retention. On the DP-900 exam, this scenario tests your understanding of matching storage services to access patterns: low-latency NoSQL for real-time queries versus cheap, durable object storage for batch analytics. A common trap is choosing Event Hubs or Stream Analytics, which are for ingestion and processing, not storage. Remember the memory tip: “Hot queries go to Cosmos, cold archives go to Blob, and Data Factory moves the load.”

DP-900 Practice Question: Describe considerations for working with non-relational data on Azure

This DP-900 practice question tests your understanding of describe considerations for working with non-relational data on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

You are a data engineer for a global gaming company. The company collects telemetry data from millions of players in real time. Each telemetry event is a JSON object containing player ID, game session ID, event type, timestamp, and a payload of up to 5 KB. The data must be stored for 90 days for real-time analytics and then moved to long-term storage for 5 years for historical analysis. The real-time analytics require querying by player ID and event type with sub-second latency. The long-term storage must be cost-effective and support batch analytics. You need to design a storage solution. Which combination of Azure services should you use to meet these requirements?

Question 1hardmultiple choice

Read the full network assurance explanation →

A
Store all telemetry in Azure Data Lake Storage Gen2 with a hierarchical namespace, and use Azure Synapse Serverless SQL for real-time queries.
Why wrong: ADLS Gen2 is not optimized for sub-second queries on individual records.
B
Ingest telemetry into Azure Stream Analytics, output to Azure SQL Database for 90 days, then export to Azure Data Lake Storage.
Why wrong: Azure SQL Database is relational and may not handle JSON efficiently; sub-second latency may be challenging.
C
Ingest telemetry into Azure Event Hubs and then store in Azure Blob Storage with a lifecycle management policy to delete after 90 days.
Why wrong: Blob Storage does not provide sub-second query latency on JSON fields.
D
Ingest telemetry into Azure Cosmos DB with TTL set to 90 days, then use Azure Data Factory to copy expired data to Azure Blob Storage.
Cosmos DB provides low-latency queries; TTL automatically removes data; Blob Storage is cost-effective for long-term.

Full breakdown with real-world context →

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

✓

Ingest telemetry into Azure Cosmos DB with TTL set to 90 days, then use Azure Data Factory to copy expired data to Azure Blob Storage.

Option A is correct because Azure Cosmos DB provides sub-second query latency on player ID and event type, with a Time-to-Live (TTL) to automatically expire data after 90 days. Then, you can use Azure Data Factory to copy the expired data to Azure Blob Storage (Cool or Archive) for cost-effective long-term storage. Option B is wrong because Azure Event Hubs is for ingestion, not storage. Option C is wrong because Azure Stream Analytics is for processing, not storage. Option D is wrong because Azure Data Lake Storage is for analytics, but for real-time sub-second queries, Cosmos DB is better.

Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

✗
Store all telemetry in Azure Data Lake Storage Gen2 with a hierarchical namespace, and use Azure Synapse Serverless SQL for real-time queries.
Why it's wrong here
ADLS Gen2 is not optimized for sub-second queries on individual records.
✗
Ingest telemetry into Azure Stream Analytics, output to Azure SQL Database for 90 days, then export to Azure Data Lake Storage.
Why it's wrong here
Azure SQL Database is relational and may not handle JSON efficiently; sub-second latency may be challenging.
✗
Ingest telemetry into Azure Event Hubs and then store in Azure Blob Storage with a lifecycle management policy to delete after 90 days.
Why it's wrong here
Blob Storage does not provide sub-second query latency on JSON fields.
✓
Ingest telemetry into Azure Cosmos DB with TTL set to 90 days, then use Azure Data Factory to copy expired data to Azure Blob Storage.
Why this is correct
Cosmos DB provides low-latency queries; TTL automatically removes data; Blob Storage is cost-effective for long-term.
Related concept
Static NAT maps one inside address to one outside address.

Common exam traps

Common exam trap: NAT rules depend on direction and matching traffic

NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.

Detailed technical explanation

How to think about this question

NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.

KKey Concepts to Remember

Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.

TExam Day Tips

Identify inside and outside interfaces first.
Check whether the scenario needs static NAT, dynamic NAT or PAT.
Do not confuse NAT matching ACLs with normal packet-filtering intent.

Key takeaway

NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.

Real-world example

How this comes up in practice

A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.

What to study next

Got this wrong? Here's your next step.

Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related DP-900 NAT questions on configuration and troubleshooting.

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FAQ

Questions learners often ask

What does this DP-900 question test?

Describe considerations for working with non-relational data on Azure — This question tests Describe considerations for working with non-relational data on Azure — Static NAT maps one inside address to one outside address..

What is the correct answer to this question?

The correct answer is: Ingest telemetry into Azure Cosmos DB with TTL set to 90 days, then use Azure Data Factory to copy expired data to Azure Blob Storage. — Option A is correct because Azure Cosmos DB provides sub-second query latency on player ID and event type, with a Time-to-Live (TTL) to automatically expire data after 90 days. Then, you can use Azure Data Factory to copy the expired data to Azure Blob Storage (Cool or Archive) for cost-effective long-term storage. Option B is wrong because Azure Event Hubs is for ingestion, not storage. Option C is wrong because Azure Stream Analytics is for processing, not storage. Option D is wrong because Azure Data Lake Storage is for analytics, but for real-time sub-second queries, Cosmos DB is better.

What should I do if I get this DP-900 question wrong?

What is the key concept behind this question?

Static NAT maps one inside address to one outside address.

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