FABRIC DP-600 EXAM QUESTIONS & ANSWERS
1. One lake - Answer - -Built on ADLS Gen2, one copy of the data which everything accesses, hierarchical
namespace, automatically indexed
-One onelake per tenant
-supports all types of data (structured, semi, and unstructured)
2. Partition - Answer - § Nothing more than breaking up a large table into smaller more manageable chunks
based on a criterion (could be by year for instance), improving query performance and manageability
§ distributes data to improve performance and scalability
§ Data is more organized and easier to retrieve
§ Partitioning is done using the primary key of the fact table
Doesn't aggregate rows like a group by, this does the count of what an aggregation would be if doing a group by,
replicated for each individual row (Kevin - 3 (males), Kevin - 3 (males), Kevin - 3(males), Karina -1 (female)
§ You can partition by year, and then you will get a ditterence number of rows per year (unlike indexing which will have the
same number of rows per index)
3. Workspace level permissions (order of privilege) - Answer - o Admin -
§ can do everything (share, add/review people, allow reshare)
§ Add/delete workspaces
and people, o Member
§ Least privilege role who can allow other members to reshare is member *she said good question for exam*
§ Least privilege role for adding members too
o Contributor
o Viewer
§ Only for reading/viewing execution of notebooks/pipelines, reading KQL databases, and connecting to SQL Endpoints
(allowed)
§ viewer role does NOT provide permission to read the lakehouse data through Lakehouse explorer
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, 4. Parquet File Format - Answer - § Columner oriented file format
§ This is the preferred file type since it is optimized for large scale big data
§ Better at saving storage space than traditional row file formats
o Data in a fabric wq43house is stored as a **PARQUET FILE FORMAT IN A ONELAKE** (she repeated multiple times)
5. Delta table **Key feature of fabric lakehouse** - Answer - § Delta format for durability and
scale
§ Optimize read and write with v-Order and optimized writes
· Creates fewer, larger parquet files
6. Pro feature (that PPU doesn't have) - Answer - Share non-Power BI Fabric items
2/
15
1. One lake - Answer - -Built on ADLS Gen2, one copy of the data which everything accesses, hierarchical
namespace, automatically indexed
-One onelake per tenant
-supports all types of data (structured, semi, and unstructured)
2. Partition - Answer - § Nothing more than breaking up a large table into smaller more manageable chunks
based on a criterion (could be by year for instance), improving query performance and manageability
§ distributes data to improve performance and scalability
§ Data is more organized and easier to retrieve
§ Partitioning is done using the primary key of the fact table
Doesn't aggregate rows like a group by, this does the count of what an aggregation would be if doing a group by,
replicated for each individual row (Kevin - 3 (males), Kevin - 3 (males), Kevin - 3(males), Karina -1 (female)
§ You can partition by year, and then you will get a ditterence number of rows per year (unlike indexing which will have the
same number of rows per index)
3. Workspace level permissions (order of privilege) - Answer - o Admin -
§ can do everything (share, add/review people, allow reshare)
§ Add/delete workspaces
and people, o Member
§ Least privilege role who can allow other members to reshare is member *she said good question for exam*
§ Least privilege role for adding members too
o Contributor
o Viewer
§ Only for reading/viewing execution of notebooks/pipelines, reading KQL databases, and connecting to SQL Endpoints
(allowed)
§ viewer role does NOT provide permission to read the lakehouse data through Lakehouse explorer
1/
15
, 4. Parquet File Format - Answer - § Columner oriented file format
§ This is the preferred file type since it is optimized for large scale big data
§ Better at saving storage space than traditional row file formats
o Data in a fabric wq43house is stored as a **PARQUET FILE FORMAT IN A ONELAKE** (she repeated multiple times)
5. Delta table **Key feature of fabric lakehouse** - Answer - § Delta format for durability and
scale
§ Optimize read and write with v-Order and optimized writes
· Creates fewer, larger parquet files
6. Pro feature (that PPU doesn't have) - Answer - Share non-Power BI Fabric items
2/
15
