Free Microsoft Data Engineering DP-700 Actual Exam Questions

A. High concurrency is the only option that actually allows sharing the same Spark session among notebooks. The others are about resource management, not session sharing.

I’m with the folks saying A. High concurrency is the feature that actually lets multiple notebooks connect to the same Spark session, which is exactly what the question asks for. The other options just tweak performance or resource allocation but don’t enable session sharing between notebooks. So, A makes the most sense here.

It’s A. If the goal is to handle old files without just deleting, copying them somewhere else first makes sense. B and C are more about managing data inside a Delta Lake, so they’d only fit if that’s the format here. D is too final if we need to keep files around somehow. Since the question says “handling” old files but doesn’t confirm deletion, copying with a data pipeline covers archiving or moving without losing data.

If the goal is just to remove old files entirely, D seems straightforward since it deletes them. No extra steps like copying or optimizing needed if cleanup is the main focus. D

It’s C, smaller keys cut storage and speed up joins without extra cost.

B vs C, increasing capacity might help speed but changing keys could reduce storage and improve performance more sustainably.

Maybe A could work if the book reviews need complex transformations or real-time processing, but since that’s not specified, it feels a bit over the top. D seems more straightforward for just moving and processing data in batches, so I’m ruling out B and C since they don’t really address data processing. If the question was about just sharing data externally, C might fit, but it’s about implementing a solution, so D still makes the most sense here.

It’s D because a data pipeline handles batch processing well without extra complexity.

D. The key here is you need something that can dynamically filter rows based on who's querying, and functions are designed for that in row-level security setups. Constraints (B) are more about data integrity, not filtering access. Procedures (A) don't filter data on select queries, and schemas (C) are more about organizing objects, not controlling access at the row level. So the function is what actually applies the security logic per user.

D, since functions define the filtering logic needed for row-level security.

C, since domain contributor covers creation without full admin rights, fitting least privilege.

Assigning workspaces to subdomains seems like a step beyond just creating them, so domain contributor might not cover it fully. Domain admin probably fits better for managing both creation and assignments while still scoped to domains.

A/C? Deleting or backing up first covers all bases if overwrite isn’t default.

It’s D because items paired by name usually get overwritten automatically during deployment.

If we think about the purpose of each option, VACUUM (A) mainly removes old files no longer needed, so it’s not really about consolidating files. BROADCAST (B) and CACHE (D) are about query performance, not file management. So, even if we don’t know for sure that Table1 is Delta, the only command focused on merging small files into bigger ones is OPTIMIZE (C). Does anyone know if Fabric enforces tables to be Delta by default? That could settle this clearly.

A/B? VACUUM (A) removes old files but doesn’t merge Parquet files, so probably not that. BROADCAST (B) has nothing to do with file consolidation; it’s for join optimization. OPTIMIZE (C) is specifically for merging small files in Delta tables, which sounds exactly like what’s needed here. CACHE (D) just puts data in memory, not physical file consolidation. So between those, C still seems the way to go given the goal is consolidation of Parquet files.

It’s A because direct sharing with read access via SQL endpoint limits User1 to just query data without broader workspace or Spark permissions, keeping the access tightly scoped as required.

It’s A because sharing Lakehouse1 directly with SQL read access avoids workspace roles that expose other items or Spark capabilities. This keeps User1’s access narrow and secure.

Dataset1 fits relational table since it has a neat integer key and simple structure.

For Dataset1, the unique integer key and simple incremental nature work well with a relational table, making development straightforward. Dataset2’s semi-structured data and bulk transfer combined with custom visuals suggest a lake database, since it handles unstructured data and supports those visuals better. Dataset3’s bulk load and use of row-based window functions definitely call for a data warehouse item, which optimizes those analytic operations during loading. This setup uses built-in capabilities efficiently without overcomplicating the process.

I’m thinking option D makes sense to combine ProductCategory and ProductSubcategory into a single dimension since those are closely related hierarchies. It cuts down on the number of joins and fits the star schema approach. For the fact table, A is the obvious choice since SalesOrder contains transactional data with measures. This way, you keep the design simple and efficient without extra development work. The other options don’t really group the dimension tables well or miss the clear fact table candidate.

I see why A for SalesOrder as the fact table works since it has the measures. For dimensions, D is good because it combines those related tables into one, cutting down on joins and complexity.

For Notebook1, scheduling it at 8 AM weekdays makes sense. Notebook2 should use the event trigger tied to the blob save. Model1 refresh looks like it needs a dependent trigger after Notebook1 runs successfully.

I agree with scheduling Notebook1 and event-triggering Notebook2; Model1 refresh depends on Notebook1’s success.

I also think the batch processing angle is strong here since checkpointing usually isn’t part of streaming setups. Plus, the code doesn’t show any triggers or listeners that’d indicate real-time data flow, so No for streaming feels right. On schema updates, there’s no explicit handling in the snippet, so that’s another No from me. The focus seems more on loading and saving states rather than evolving schemas or real-time ingestion.

I’m saying No for real-time streaming because the code looks like it’s designed for batch uploads with checkpoints, not continuous data streams. Also, No on automatic schema evolution since there’s no schema update logic shown.

I agree using @variables('tables') fits best here since no parameters are allowed. Just make sure the variable is initialized beforehand, or ForEach won’t have any tables to iterate over.

Using @variables('tables') fits since no params allowed; just ensure variable’s set first.

For BirthDate, Default mask is the go-to since it replaces the whole date with 1900-01-01. For Email, Partial mask works because it can reveal specific characters upfront and at the end, matching the requirement closely.

For BirthDate, the Default mask fits perfectly since it can replace the entire value with a fixed date like 1900-01-01, matching the requirement exactly. For Email, Partial mask seems right because it can reveal parts of the string, but I'm not sure if it allows showing exactly one leading and five trailing characters or if it’s more limited to something like first and last characters only. Still, other masking options don’t give that kind of partial reveal, so Partial is the best fit here despite the exact parameter question.