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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prepare and process data | 30-35% | - Ingest and transform data
|
| Topic 2: Set up and configure an Azure Databricks environment | 15-20% | - Create and configure Azure Databricks workspaces
|
| Topic 3: Deploy and maintain data pipelines and workloads | 30-35% | - Manage production workloads
|
| Topic 4: Secure and govern Unity Catalog objects | 15-20% | - Implement governance and security
|
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NEW QUESTION # 50
You have an Azure Databricks workspace that is enabled for Unity Catalog. You plan to run the following PySpark code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
For HOTSPOT questions, each statement must be evaluated against the actual PySpark code shown in the answer area. Key evaluation principles:
DataFrames are immutable - every transformation returns a new DataFrame; the original is unchanged.
Transformations (filter, select, groupBy, join) are lazy and only execute when an action (show, count, write) is called.
Null handling: df.filter(col != None) is incorrect in PySpark due to SQL null semantics; use col.isNotNull() or dropna() instead. Schema changes: using mergeSchema=true or schema evolution handles new columns.
Write modes: ' overwrite ' replaces existing data; ' append ' adds to it.
Always check whether the code uses the correct Delta format (.format( ' delta ' )), Unity Catalog three-part naming, and whether write operations include a checkpointLocation for streaming queries. Evaluate each statement strictly on what the code does, not on what it might intend to do.
Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics
NEW QUESTION # 51
You manage Declarative Automation Bundles by using the Databricks CLI.
You run the following command in a terminal window.
databricks bundle init
What occurs when you run the command?
Answer: B
Explanation:
The databricks bundle init command initializes a new bundle project from a selected default or custom project template. It prompts for relevant project values and creates the local project structure and starter configuration files. It does not deploy resources; deployment is performed with databricks bundle deploy. It does not merely validate an existing configuration, because validation uses databricks bundle validate. Running deployed bundle resources requires databricks bundle run followed by the resource key. The distinction matters in an automated bundle lifecycle: initialization scaffolds the project, validation checks configuration, deployment creates or updates workspace resources, and run starts a configured job or pipeline. Therefore, option A accurately describes the initialization command's function. Microsoft Learn
NEW QUESTION # 52
You have an Azure Databricks workspace that contains the objects shown in the following table.
Name | Type
Catalog1 | Catalog
Schema1 | Schema
Sales1 | Table
Notebook1 | Notebook
Space1 | AI/BI Genie space
Users often use the following words to refer to a sale: transaction, event, order, and invoice.
You need to create a knowledge store. The solution must ensure that when the users use any of the words in Space1, Genie queries the Sales1 table. Any other Genie spaces must remain unaffected.
To which object should you add the instructions?
Answer: A
Explanation:
The instructions must be added to Space1 because a Genie knowledge store is scoped to the individual Genie space, now called a Genie Agent. Adding synonyms and business-language instructions there teaches Space1 that "transaction," "event," "order," and "invoice" refer to sales information in Sales1. The configuration affects only that Genie space, satisfying the requirement that other spaces remain unchanged. Adding instructions to Sales1 or Schema1 would modify shared Unity Catalog metadata and could affect other consumers of those objects. Notebook1 is unrelated to the semantic instructions used by Genie when converting natural-language questions into SQL. Genie knowledge stores contain space-specific definitions, synonyms, join relationships, SQL expressions, and prompt-matching guidance without changing the underlying Unity Catalog objects. Microsoft Learn
NEW QUESTION # 53
You have an Azure Databricks workspace that is enabled for Unity Catalog and contains a Delta table named Orders.
You load the Orders table into an Apache Spark DataFrame named df.
You need to create a DataFrame that excludes rows where the order amount is null.
Solution: You run the following expression.
df.filter(df.order_amount.isNotNull())
Does this meet the goal?
Answer: B
Explanation:
The correct answer is A - Yes.
df.filter(df.order_amount.isNotNull()) is the correct PySpark pattern for excluding null rows. The isNotNull() method is a Column method that returns True for every row where order_amount has a value and False for rows where it is null. Spark ' s filter keeps only the rows where the condition evaluates to True, producing a DataFrame with all null order_amount rows removed.
This works correctly because isNotNull() is explicitly null-aware - unlike the != None comparison in Q52, it doesn ' t rely on Python equality semantics. Under the hood it maps to the SQL expression order_amount IS NOT NULL, which is unambiguous in both SQL and Spark.
Both df.filter(df.order_amount.isNotNull()) and df.dropna(subset=[ ' order_amount ' ]) produce identical results. The choice between them is stylistic - isNotNull() reads more explicitly as a filter condition, while dropna is more compact when handling multiple columns.
Reference: https://learn.microsoft.com/en-us/azure/databricks/pyspark/basics
NEW QUESTION # 54
What happens if incoming data violates Delta table schema?
Answer: D
Explanation:
Delta Lake enforces schema by default. If incoming data does not match schema, the write operation fails unless schema evolution is explicitly enabled. It does not auto-cast or append invalid data. Overwriting would require explicit command.
NEW QUESTION # 55
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