Databricks-DE-Pro Practice Test 6 — 10 Questions

Question 1

Which THREE features are provided by Delta Lake when compared to standard Parquet files? (Select THREE)

Accepted Answer

ACID transactions. Delta Lake adds a transaction log (the _delta_log) to standard Parquet files, enabling ACID transactions, time travel, and schema enforcement. These features solve common data engineering pain points such as partial writes, data corruption, and the inability to revert to previous versions of data. Mastering these capabilities is essential for building reliable data lakes that provide the same consistency and reliability as traditional data warehouses.

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Automatic indexing for all columns

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Built-in support for real-time web socket streaming

Question 2

What is the primary function of a 'Personal Access Token' (PAT) in Databricks, and why is it considered a security risk if not managed properly?

Accepted Answer

It provides long-lived programmatic access to the API.. Personal Access Tokens (PATs) are used for programmatic access to Databricks REST APIs. They authenticate the user and inherit their permissions. The risk lies in their longevity; if an unexpired token is leaked, an attacker can impersonate the user without needing to re-authenticate via SSO. Therefore, limiting token duration and using service principals for automated tasks are crucial security measures to protect the platform.

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It allows users to bypass multi-factor authentication.

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It is intended for end-user dashboard access.

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It encrypts data stored in the workspace.

Question 3

Refer to the exhibit. A Databricks administrator wants to restrict access to a specific SQL Alert. Based on the JSON policy, which statement accurately describes the current permission model for this alert?

Accepted Answer

The user can see the alert, but cannot trigger a manual refresh or modify the alert settings.. The exhibit displays an Access Control List (ACL) entry granting 'CAN_VIEW' permission to a specific user for a single alert resource. In Databricks, SQL Alerts are secured via the workspace-level permission model. Understanding these JSON-based policy definitions is essential for data engineers tasked with auditing governance and ensuring that sensitive monitoring data is only accessible to authorized personnel during security reviews.

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The user is allowed to modify the alert's query criteria and notification frequency.

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The user has sufficient permissions to delete the alert from the Databricks SQL workspace.

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This JSON policy provides the user with global access to all alerts within the SQL warehouse.

Question 4

Your organization is ingesting sensitive PII data. You need to ensure that personal identifiers are masked during the ingestion process before they are stored in the Bronze layer of your Medallion architecture. What is the best practice for this?

Accepted Answer

Apply masking logic within the initial streaming ingestion transformation.. Implementing masking at the ingestion layer using Delta Live Tables (DLT) expectations or standard Spark transformations ensures that sensitive data is never written in plaintext to the Bronze layer. Applying transformations during the 'Acquisition' phase is a critical security practice, ensuring that governance requirements are met before data becomes available to analysts, thereby reducing the risk of accidental exposure and maintaining data privacy compliance across the pipeline.

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Mask the data using Delta Lake column masking after the data reaches the Silver layer.

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Configure Unity Catalog to mask columns only for specific users.

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Use a post-ingestion job to delete PII columns.

Question 5

What is the primary benefit of the Medallion architecture in a Databricks Lakehouse?

Accepted Answer

It provides a clear progression of data quality and structure.. The Medallion architecture provides a clear, structured progression of data quality. By segregating data into Bronze (raw), Silver (cleaned), and Gold (refined) layers, organizations can maintain an immutable audit trail while enabling both technical teams and business analysts to consume the data at the appropriate level of abstraction. This structure simplifies data governance, incremental processing, and data quality management, which are fundamental to building a reliable Lakehouse.

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It eliminates the need for data partitioning and Z-Ordering.

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It forces all data to be stored in a star schema at all layers.

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It automatically converts all incoming data to a structured format.

Question 6

Which metric should a data engineer prioritize when investigating a slow-running query in the Databricks SQL query history?

Accepted Answer

The total execution time and breakdown of the query stages.. The 'Total Time' metric is decomposed into wait time, compilation time, and execution time. Identifying the execution time bottleneck helps determine if the issue is compute-bound, I/O-bound, or due to slow data retrieval. This information is vital for deciding whether to optimize the query code, adjust partitioning, or scale the warehouse, ensuring that engineering efforts are targeted at the right performance issues to maximize impact.

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The number of users logged into the workspace at the time.

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The color of the query status light in the UI.

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The name of the user who submitted the query.

Question 7

Refer to the exhibit. A Data Engineer is attempting to merge data into a table with these constraints defined. If the incoming batch contains rows that violate these rules, what is the default behavior of the Delta Lake engine during the merge operation?

Accepted Answer

The transaction fails entirely, and no changes are committed to the Delta table.. Delta Lake maintains strict ACID compliance. When a transaction violates defined CHECK or NOT NULL constraints, the engine throws an error, and the entire transaction is rolled back. This behavior prevents data corruption and ensures that only valid state transitions occur. This is essential for maintaining data integrity in complex ETL pipelines where maintaining a 'known good' state is preferred over partial updates or silent failures.

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The engine silently ignores the invalid rows and completes the merge for valid records only.

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The invalid rows are automatically routed to a hidden sidecar table for later review.

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The engine automatically updates the invalid rows to the default column value.

Question 8

A Data Engineer is implementing a medallion architecture. Which THREE steps are critical for effectively implementing a high-quality 'Silver' layer from 'Bronze' data?

Accepted Answer

Enforce strict schema validation on incoming Bronze files.. The Silver layer is the 'single source of truth' for refined data. By applying strict schema enforcement, deduplication, and standardized data types, you ensure that analytical tools receive reliable, high-quality information. Proper implementation here prevents the 'garbage in, garbage out' scenario, allowing data scientists and analysts to focus on modeling and reporting rather than constant, redundant data cleaning tasks, significantly increasing organizational productivity and trust in data.

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Apply business logic and complex transformations to create aggregate summary tables.

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Convert all column names to uppercase to ensure case-insensitive consistency.

Question 9

You need to perform a deduplication task on a streaming source that includes late-arriving data. Which Delta Lake feature is best suited to manage this while ensuring efficient state cleanup?

Accepted Answer

Use the dropDuplicates() method with watermark settings to manage state.. The `withWatermark` and `dropDuplicates` combination is specifically designed for streaming deduplication. Watermarks tell the engine how long to wait for late-arriving data, allowing it to clear old state from memory. This is critical for high-volume streaming jobs where state growth would otherwise lead to out-of-memory errors or significant performance degradation, ensuring the job remains stable over long periods of execution.

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Use a standard SQL DELETE query with a subquery to identify and remove duplicates.

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Set the table property 'delta.enableChangeDataFeed' to true and filter on the change log.

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Increase the 'spark.sql.shuffle.partitions' setting to ensure all duplicates land on the same node.

Question 10

Which of the following describes the correct behavior of Unity Catalog's 'Data Lineage' when used for security compliance?

Accepted Answer

It captures table-to-table data dependencies.. Data Lineage in Unity Catalog automatically captures the flow of data from source to target. This is invaluable for security compliance, as it allows administrators to trace sensitive data across the environment, identify which tables are derived from PII sources, and understand the impact of modifying or deleting specific tables. By visualizing these dependencies, teams can ensure that security policies are consistently applied throughout the data lifecycle.

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It shows which users accessed the data.

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It manually triggers an alert when PII is detected.

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It permanently stores the raw data content.