FABRIC DP-600 EXAM QUESTIONS WITH DETAILED
VERIFIED ANSWERS (100% CORRECT ANSWERS)
/ALREADY GRADED
One lake Ans✓✓✓-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)
Partition Ans✓✓✓§ 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 difference number
of rows per year (unlike indexing which will have the same number of
rows per index)
Workspace level permissions (order of privilege) Ans✓✓✓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
Parquet File Format Ans✓✓✓§ 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)
Delta table **Key feature of fabric lakehouse** Ans✓✓✓§ Delta format
for durability and scale
§ Optimize read and write with v-Order and optimized writes
· Creates fewer, larger parquet files
Pro feature (that PPU doesn't have) Ans✓✓✓Share non-Power BI Fabric
items
VERIFIED ANSWERS (100% CORRECT ANSWERS)
/ALREADY GRADED
One lake Ans✓✓✓-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)
Partition Ans✓✓✓§ 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 difference number
of rows per year (unlike indexing which will have the same number of
rows per index)
Workspace level permissions (order of privilege) Ans✓✓✓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
Parquet File Format Ans✓✓✓§ 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)
Delta table **Key feature of fabric lakehouse** Ans✓✓✓§ Delta format
for durability and scale
§ Optimize read and write with v-Order and optimized writes
· Creates fewer, larger parquet files
Pro feature (that PPU doesn't have) Ans✓✓✓Share non-Power BI Fabric
items
