Databricks-DE-Pro Practice Test 8 — 10 Questions

Question 1

When designing a streaming pipeline using Structured Streaming, which THREE of the following are necessary to ensure 'exactly-once' processing semantics in Databricks?

Accepted Answer

Using a source system that supports replaying data (e.g., Kafka or Delta).. Exactly-once processing requires that the source system, the processing engine, and the sink system all support fault-tolerant checkpoints and idempotency. Databricks handles this through checkpointing and write-ahead logs. Understanding these components is critical for data engineers to ensure data consistency in critical financial or operational systems, preventing the common pitfalls of duplicate entries or missed data during cluster restarts or transient failures.

Answer

Setting 'spark.sql.shuffle.partitions' to 1.

Answer

Disabling the Delta Lake write-ahead log.

Question 2

A Data Engineer is using Databricks Asset Bundles (DABs) to manage a project. Where should the engineer define the job settings, such as clusters, schedules, and task dependencies?

Accepted Answer

In a databricks.yml file within the project root.. Databricks Asset Bundles use YAML files, specifically those located in the bundles configuration folder, to manage infrastructure as code. Defining job settings in the `databricks.yml` file allows for version control and consistent deployments across different environments (dev, staging, prod). This approach ensures that the entire project state is replicable and managed through a standardized CI/CD pipeline, reducing manual configuration errors and increasing the speed of environment deployments.

Answer

In a hardcoded JSON file located in the /tmp/ directory.

Answer

Inside the Python code using the dbutils.job() library.

Answer

By manually clicking through the Databricks UI after deployment.

Question 3

Refer to the exhibit. An engineer has configured the cluster settings as shown. What is the expected impact on the Delta table's performance and write operations?

Accepted Answer

Read performance will improve over time as files are automatically compacted and optimized for downstream queries.. The settings `optimizeWrite` and `autoCompact` are crucial for maintaining healthy Delta tables. `optimizeWrite` rearranges data for optimal file size before writing, while `autoCompact` automatically merges small files after writes. These configurations reduce the need for manual maintenance, lower storage costs, and ensure read queries perform optimally by preventing file fragmentation. This proactive approach is standard practice for high-performance Delta Lake implementations that minimize manual intervention.

Answer

Write latency will increase, but read performance will remain unaffected.

Answer

Storage costs will increase significantly due to the creation of temporary duplicate data files.

Answer

The cluster will fail to start because these configurations are deprecated in recent Databricks runtimes.

Question 4

A data engineer is tasked with ensuring that sensitive information in a 'customer' table is masked for all users except the 'Data_Science' group. What is the correct Unity Catalog feature to implement?

Accepted Answer

Apply a masking policy using a SQL function to the table column.. Dynamic data masking in Unity Catalog allows for the creation of masking functions that return obfuscated values based on the current user's role. By assigning these functions to columns, administrators ensure that sensitive data is protected while remaining available for authorized users. This approach is highly effective for maintaining data usability without compromising the security of PII.

Answer

Create a view that performs a CASE statement to mask the column.

Answer

Use the 'DROP COLUMN' command to remove sensitive columns.

Answer

Encrypt the column using an external library before writing to Delta.

Question 5

Refer to the exhibit. An engineer observes that queries filtering on 'customer_id' are running slowly despite Z-Ordering. What is the most likely cause?

Accepted Answer

The queries lack filters on 'region' or 'date', preventing effective partition pruning.. The exhibit shows that the table is partitioned by 'region' and 'date', while Z-Ordering is applied to 'customer_id'. If queries filter on 'customer_id' but do not provide 'region' or 'date', the engine must scan all partitions. Z-Ordering is only effective within each partition. If the data is not well-clustered or the partitions are too large, the engine cannot skip files effectively, leading to high latency during execution.

Answer

The partition columns should include 'customer_id' to improve the pruning speed.

Answer

Z-Ordering must be performed on the partition columns instead of the join columns.

Answer

The file format should be changed to Parquet to improve individual file read performance.

Question 6

A data scientist reports that their notebook takes 20 minutes to initialize, even when the cluster is running. What is the most likely reason for this high initialization time?

Accepted Answer

The notebook is installing many libraries using %pip install.. High initialization time in a running cluster is frequently caused by excessive libraries being installed at the notebook level via '%pip install'. Every time the notebook is attached or the context is refreshed, these libraries must be re-resolved and installed, which adds significant overhead. Pre-installing these libraries at the cluster level via 'cluster-scoped libraries' allows them to be available immediately upon startup, eliminating this redundant installation phase and significantly speeding up the initialization process.

Answer

The cluster is using spot instances.

Answer

The cluster has too many workers allocated.

Answer

The user is using the wrong Databricks Runtime version.

Question 7

You are optimizing a PySpark job that reads from a Delta table. You notice skewed data distribution on the 'customer_id' column, causing Task-level stragglers. Which transformation should you apply to the DataFrame to mitigate this skew during a join operation?

Accepted Answer

Salt the skewed column and perform the join.. Salting involves adding a random prefix to the join key of the skewed table and exploding the join key of the dimension table. This technique breaks down massive partitions caused by skewed keys into smaller, evenly distributed chunks. Understanding data skew is critical in Databricks for maintaining cluster stability and preventing OOM errors, ensuring that shuffle partitions are utilized efficiently across all available executors rather than overloading one core.

Answer

Increase the spark.sql.shuffle.partitions configuration dynamically.

Answer

Broadcast the skewed table to all executors.

Answer

Cache the skewed table in memory before the join.

Question 8

When using the Unity Catalog, how should an engineer properly reference a table named 'sales' located in the 'finance' schema within the 'prod_catalog' catalog using Spark SQL?

Accepted Answer

SELECT * FROM prod_catalog.finance.sales. Unity Catalog uses a three-level namespace hierarchy: catalog.schema.table. This structure provides consistent governance and discovery across the entire Databricks account. Proper referencing requires the fully qualified name to ensure Spark identifies the exact object within the catalog's permission model. This implementation is crucial for security and multi-tenant environments where namespaces must be strictly isolated to prevent unauthorized access and data leakage across production and development environments.

Answer

SELECT * FROM finance.sales

Answer

SELECT * FROM sales

Answer

SELECT * FROM catalog_prod.finance.sales

Question 9

A Data Engineer wants to monitor data quality trends over time for a critical table. Which tool provides the most native and easy-to-use visualization of these metrics?

Accepted Answer

Create a Databricks SQL dashboard using the 'expectations' table provided by DLT.. Delta Lake's integration with Databricks SQL and DLT provides native dashboards that track data quality metrics automatically. Using Expectation history logs, engineers can visualize failure rates and quality trends without building custom monitoring solutions. This visibility is essential for proactive maintenance, allowing teams to catch degrading data quality before it impacts critical downstream business reporting, ensuring higher service levels for end-users.

Answer

Export all data quality logs to an external S3 bucket and use a third-party BI tool.

Answer

Write a custom Spark job to parse the Delta transaction log and send alerts via email.

Answer

Manually query the 'information_schema' every hour to track table row counts.

Question 10

A data engineer wants to monitor the health of Delta Live Tables (DLT) pipelines and be alerted if a pipeline fails. Which approach is the most efficient and native way to achieve this?

Accepted Answer

Define a Notification Destination in the DLT pipeline settings and associate it with the 'On failure' event.. Using Notification Destinations in the DLT pipeline settings is the most native method. It allows engineers to configure email or Slack alerts directly within the UI or JSON configuration. This is crucial for production reliability, ensuring that stakeholders receive immediate notifications regarding job failures or data quality issues without needing external orchestration or custom API scripts, maintaining observability across the entire pipeline lifecycle.

Answer

Configure an external cron job to poll the Databricks Jobs API every minute for status updates.

Answer

Enable the 'Log to Workspace' feature and manually query the system logs using SQL every hour.

Answer

Use the Databricks SQL Alerts tool to monitor the underlying Delta tables for new record counts.