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Describe an analytics workload on Azure →hardMultiple ChoiceObjective-mapped

DP-900 Describe an analytics workload on Azure Practice Question

This DP-900 practice question tests your understanding of describe an analytics workload on azure. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.

Exhibit

{
  "version": "1.0",
  "aggregations": [
    {
      "table": "Sales",
      "measure": "SUM(Amount)",
      "dimensions": ["ProductCategory", "Region", "Date"],
      "aggregationLevel": "Monthly"
    }
  ]
}

Refer to the exhibit. An administrator is configuring aggregations in Power BI Premium to improve performance on a large dataset. The aggregation is defined on the Sales table with SUM(Amount) grouped by ProductCategory, Region, and Date at the monthly level. However, some reports that query daily data are still slow. What is the most likely reason?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

  • Clue: "most likely"

    Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

Question 1hardmultiple choice
Full question →

Exhibit

{
  "version": "1.0",
  "aggregations": [
    {
      "table": "Sales",
      "measure": "SUM(Amount)",
      "dimensions": ["ProductCategory", "Region", "Date"],
      "aggregationLevel": "Monthly"
    }
  ]
}

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

✓

The aggregation level is monthly, but queries need daily granularity

Option C is correct because the aggregation is defined at the monthly level (grouping by month), but the slow reports are querying daily data. Power BI aggregations work by pre-aggregating data at a specified granularity; when a query requests a lower level of detail (daily), the aggregation cannot satisfy the query, so Power BI falls back to scanning the full detailed dataset, causing slow performance. To improve daily queries, an additional aggregation at the daily level would be needed.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

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

  • ✗

    The dataset is too large for aggregations

    Why it's wrong here

    Aggregations are designed for large datasets; size is not the issue.

  • ✗

    The aggregation is not in DirectQuery mode

    Why it's wrong here

    Aggregations work with both import and DirectQuery; mode is not the issue.

  • ✓

    The aggregation level is monthly, but queries need daily granularity

    Why this is correct

    Daily queries cannot use the monthly aggregation, so they hit the full dataset.

    Clue confirmation

    The clue word "most likely" in the question point toward this answer.

    Related concept

    Read the scenario before looking for a memorised answer.

  • ✗

    The aggregation has too many dimensions

    Why it's wrong here

    The number of dimensions is fine; the issue is the aggregation level.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates may think aggregations automatically speed up all queries on a table, but they must match the exact granularity of the query; otherwise, the aggregation is ignored and the full dataset is scanned.

Detailed technical explanation

How to think about this question

Under the hood, Power BI aggregations use a technique called 'user-defined aggregations' where pre-computed summary tables are stored in-memory (for Import mode) or in the source (for DirectQuery). When a query is executed, the Power BI engine checks if the aggregation table can answer the query at the requested granularity; if not, it performs a 'query folding' fallback to the full fact table. In real-world scenarios, a common pattern is to create multiple aggregations at different granularities (e.g., daily, monthly, yearly) and let the engine automatically select the most granular one that satisfies the query, a process known as 'aggregation table matching'.

KKey Concepts to Remember

  • Read the scenario before looking for a memorised answer.
  • Find the constraint that changes the correct option.
  • Eliminate answers that are true in general but not in this case.

TExam Day Tips

  • Watch for words such as best, first, most likely and least administrative effort.
  • Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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FAQ

Questions learners often ask

What does this DP-900 question test?

Describe an analytics workload on Azure — This question tests Describe an analytics workload on Azure — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: The aggregation level is monthly, but queries need daily granularity — Option C is correct because the aggregation is defined at the monthly level (grouping by month), but the slow reports are querying daily data. Power BI aggregations work by pre-aggregating data at a specified granularity; when a query requests a lower level of detail (daily), the aggregation cannot satisfy the query, so Power BI falls back to scanning the full detailed dataset, causing slow performance. To improve daily queries, an additional aggregation at the daily level would be needed.

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

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

Are there clue words in this question I should notice?

Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Last reviewed: Jun 24, 2026

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