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| Section | Weight | Objectives |
|---|---|---|
| Secure and govern data using Unity Catalog | 15-20% | - Data governance fundamentals
|
| Configure and manage Azure Databricks environments | 15-20% | - Workspace and compute configuration
|
| Deploy and manage data pipelines and workloads | 30-35% | - Pipeline design and orchestration
|
| Prepare and process data | 30-35% | - Data quality and validation
|
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NEW QUESTION # 78
You have an Azure Databricks workspace that is enabled for Unity Catalog.
You have a CSV file stored in an Azure Data Lake Storage Gen2 container.
You plan to ingest the data into an existing table by running the following SQL statement:
COPY INTO Customer
FROM ' abfss://container1@storageaccount.dfs.core.windows.net/data/customer ' FILEFORMAT = CSV; You need to ensure that the statement can access the data in the container.
What should you configure?
Answer: A
Explanation:
A Unity Catalog external location associates a cloud-storage path with a storage credential. The storage credential contains the managed identity or service principal that Azure Databricks uses to authenticate to the ADLS Gen2 account. Configuring an external location for the specified abfss path therefore enables Unity Catalog to govern access to the source files while allowing COPY INTO to read them. The user or execution identity must also have the required READ FILES permission on the external location. Microsoft Learn An external table is unnecessary because Customer already exists as the destination table, and the CSV files are being used only as the ingestion source. A Unity Catalog volume could be used if the statement referenced a /Volumes/... path, but the displayed command directly references the ADLS container through an abfss URL. A Lakeflow ingestion gateway is not used to authorize access to ADLS files for COPY INTO.
Therefore, an external location backed by a storage credential is the correct configuration.
NEW QUESTION # 79
You have an Azure Databricks workspace that contains a Delta table named Customer.
A job named Job1 performs frequent upserts into Customer.
You discover that Job1 has created many small Parquet files in Customer, and the small files are degrading query performance.
You need to improve query performance for the current data already stored in Customer. The solution must not affect the travel for the Customer table.
What should you do?
Answer: D
Explanation:
The OPTIMIZE command performs bin-packing compaction, combining the Customer table's existing small Parquet files into fewer, larger files. This reduces file-open overhead and improves data-skipping efficiency without changing the logical table contents. Enabling optimized writes affects future write operations but does not compact the small files already stored. VACUUM removes unreferenced data files that are older than the configured retention threshold; it does not reorganize active small files and can restrict time-travel availability. Reducing delta.deletedFileRetentionDuration changes how long obsolete files remain available and can directly reduce the time-travel window, contrary to the requirement. OPTIMIZE is therefore the correct operation for improving the physical layout of current data while preserving Delta table semantics and history. Microsoft Learn
NEW QUESTION # 80
You have an Apache Spark DataFrame named salesDF that contains the following columns:
* Product
* Region
* Sales
* Date
You need to create a pivot table that shows the total sales by product for each region.
How should you complete the PySpark code segment? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
* groupBy
* pivot
The groupBy( " Region " ) operation establishes one aggregation group for each geographical region in the DataFrame. The subsequent pivot( " Product " ) operation converts every distinct Product value into a separate output column. Finally, agg(F.sum( " Sales " )) calculates the total sales for every product within each region. The resulting DataFrame contains one row per region and one column for each product, with the corresponding aggregated sales value. cogroup combines grouped datasets and is not used for creating a conventional pivot table. coalesce controls the number of DataFrame partitions or replaces null values, depending on its context. The sum function is already provided inside the aggregation expression and therefore is not required in either blank.
NEW QUESTION # 81
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a catalog named Catalog1. Catalog1 contains a table named Transactions. Transactions contains the following columns:
- transaction_id
- customer_name
- email_address
- credit_card_number
- transaction_amount
You need to ensure that business analysts can query all the rows in the Transactions table. The solution must meet the following requirements:
- Prevent the analysts from seeing the full values in the email_address and credit_card_number columns.
- Ensure that the analysts can see only the values after the @
character in each email address.
- Ensure that the analysts can see only the last four digits of each
credit card number.
- Enable the analysts to query the table without errors.
- Follow the principle of least privilege.
What should you do?
Answer: A
Explanation:
To protect sensitive customer information while allowing a specific group to run queries, use Unity Catalog's Dynamic Column Masking. This grants the group table access while applying User- Defined Functions (UDFs) to redact the email addresses and credit card numbers dynamically.
Here is the exact SQL implementation to apply:
1. Create the masking SQL UDFs
Define functions to apply the exact partial masking rules requested.
2. Apply the column masks to the tableAlter the transaction table to attach these UDFs to the respective columns.
3. Grant least privilege permissionsGrant the group the SELECT permission on the table, along with the foundational permissions to access the catalog and schema.
Reference:
https://docs.databricks.com/aws/en/data-governance/unity-catalog/filters-and-masks
NEW QUESTION # 82
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a managed Delta table named Sales. Sales stores transaction data and contains the following columns:
* transactionjd (string)
* transaction date (date)
* amount (decimal)
You need to implement the following data quality requirements by using table-level data quality enforcement:
* amount must be greater than 0.
* transaction id must never be null.
* Invalid records must be rejected when data is written to the Sales table.
What should you do?
Answer: D
Explanation:
The correct answer is D - a NOT NULL constraint on transaction_id and a CHECK constraint on amount.
Delta Lake table constraints are enforced at write time by the Delta engine itself. A NOT NULL constraint rejects any INSERT or UPDATE that would place a null in transaction_id. A CHECK constraint with amount
> 0 rejects any row where amount is zero or negative. Combined, they implement exactly the stated quality rules: bad rows are rejected when data is written, not filtered away at read time.
Options A and C (SELECT with WHERE / views) are read-time constructs - they don't prevent invalid data from entering the table. A clever pipeline bypass could write directly to the table and skip the view entirely.
Option B (row-level security with WHERE conditions) is an access-control feature for restricting which rows users see, not for enforcing data quality on writes. Table constraints are the only mechanism that genuinely blocks bad data at the storage layer.
Reference: https://learn.microsoft.com/en-us/azure/databricks/delta/delta-constraints
NEW QUESTION # 83
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