Databricks-DA-Assoc Practice Test 2 — 10 Questions

Question 1

Refer to the exhibit. An analyst is reviewing a cluster configuration JSON. If the cluster is currently idle, what happens after 40 minutes of inactivity?

Accepted Answer

The cluster terminates.. Databricks clusters with auto-termination enabled will shut down after the specified inactivity threshold is met. In this configuration, the 'autotermination_minutes' is set to 30. Therefore, once the cluster remains idle for 30 minutes, it will automatically terminate. By the 40-minute mark, the cluster has already completed the termination sequence, ensuring no additional costs are incurred for idle resources beyond the defined grace period.

Answer

The cluster scales down to 2 workers.

Answer

The cluster remains idle at 2 workers.

Answer

The cluster scales up to 8 workers.

Question 2

An analyst has a Delta table `bronze.raw_events` that has accumulated many small files over months of streaming writes. They now need to optimize read performance for downstream dashboards. Which Databricks SQL command should they run?

Accepted Answer

`OPTIMIZE bronze.raw_events;`. The small-file problem from streaming writes is resolved by compaction, and OPTIMIZE performs exactly that by merging small files into larger ones. Statistics gathering and vacuuming address different concerns, and write-time properties only influence future writes rather than existing layout. Compaction is the correct maintenance action for improving scan performance on a fragmented Delta table.

Answer

`VACUUM bronze.raw_events;`

Answer

`ANALYZE TABLE bronze.raw_events COMPUTE STATISTICS;`

Answer

`ALTER TABLE bronze.raw_events SET TBLPROPERTIES ('delta.autoOptimize.optimizeWrite' = 'true');`

Question 3

A Databricks workspace administrator wants to optimize costs and manage resources effectively. Which TWO of the following capabilities allow for automatic cluster termination and scaling?

Accepted Answer

Auto-termination. Databricks provides built-in mechanisms to manage compute costs through Auto-termination and Auto-scaling. Auto-termination automatically shuts down clusters after a period of inactivity, preventing wasteful billing. Auto-scaling allows the cluster to resize itself based on the current workload requirements, adding or removing workers dynamically. These features are fundamental for maintaining a cost-efficient data platform while ensuring sufficient performance for varying data volumes during analysis.

Answer

Cluster Tagging

Answer

Delta Cache

Answer

Instance Pools

Question 4

Which TWO of the following are benefits of using Unity Catalog with Databricks SQL?

Accepted Answer

Centralized access control for all data assets.. Unity Catalog provides a unified governance layer for data and AI assets across the Databricks workspace. It enables fine-grained access control, centralized auditing, and simplified data discovery. By implementing Unity Catalog, organizations can ensure compliance and security while allowing analysts to collaborate effectively. This is a critical component of modern data platforms, providing the necessary visibility and control required by data teams to manage data assets securely at scale.

Answer

Automatic translation of SQL to Python code.

Answer

Increased query execution speed for simple SELECTs.

Answer

Ability to bypass authentication for local files.

Question 5

When importing data into Databricks, what is the primary benefit of using Delta Lake over standard Parquet files?

Accepted Answer

Delta Lake supports ACID transactions and schema enforcement.. Delta Lake adds a transaction log (the _delta_log folder) to Parquet files, enabling ACID transactions and time travel. This prevents data corruption during concurrent writes and allows users to query previous versions of data. This is a foundational concept for data reliability, as it ensures that analytical queries are always executed against a consistent and version-controlled state, which is impossible with standard, non-transactional Parquet files in a distributed system.

Answer

Delta Lake files require less storage space than Parquet.

Answer

Delta Lake is faster for reading large tables than Parquet.

Answer

Delta Lake is natively supported by all external BI tools.

Question 6

Refer to the exhibit. The table 'sales' is partitioned by 'region' and 'order_date'. How does the Databricks SQL engine process this query to ensure optimal performance?

Accepted Answer

It uses partition pruning to skip files in partitions not matching the filters. Databricks SQL utilizes partition pruning to ignore directories that do not match the WHERE clause filters. By identifying that the query filters on both 'region' and 'order_date', the engine restricts the scan to specific subdirectories on storage. This significantly reduces the amount of I/O required, which is the primary driver of performance in large-scale data lake queries, as it avoids reading irrelevant data from the storage layer.

Answer

It performs a full table scan and filters the results in memory

Answer

It re-indexes the table columns to facilitate faster lookup

Answer

It automatically broadcasts the table to all nodes for faster processing

Question 7

A data analyst is running a complex, resource-intensive query inside the Databricks SQL query editor that frequently times out before returning results. Which administrative feature should the analyst or workspace admin leverage to manage compute resources and prevent long-running queries from blocking other users?

Accepted Answer

Configure the automated query timeout setting on the SQL warehouse to automatically abort queries exceeding a specific duration.. Databricks SQL warehouses support query timeout limits and auto-stopping features that prevent runaway queries from consuming cluster resources indefinitely. Configuring these timeouts ensures cluster availability and protects shared analytical environments from deadlocks and excessive consumption, making it a critical skill for efficient query execution management.

Answer

Increase the maximum number of concurrent users in the SQL warehouse cluster settings.

Answer

Convert the query into a Spark Structured Streaming job using the Databricks notebook interface.

Answer

Manually restart the underlying Apache Spark driver node using the cluster UI restart button.

Question 8

What is the primary purpose of the Databricks Catalog Explorer?

Accepted Answer

To browse and manage metadata for catalogs, schemas, tables, and volumes.. Catalog Explorer is a unified interface within Databricks for managing and exploring the data ecosystem. It allows users to browse schemas, tables, and views, manage data permissions, and view data lineage. For a data analyst, it is the central hub for understanding data assets, verifying table metadata, and ensuring that the necessary access rights are in place before starting analysis, making it an essential tool for data discovery and governance.

Answer

To manage the deployment and monitoring of automated machine learning models.

Answer

To monitor the health and logs of active Spark clusters.

Answer

To develop and execute Python scripts in interactive notebooks.

Question 9

When creating a dashboard, an analyst needs to compare two different categories side-by-side. Which visualization is best suited for this task?

Accepted Answer

Grouped Bar Chart. A grouped bar chart is the most effective visualization for comparing distinct categories side-by-side. It allows the viewer to easily perceive differences in magnitude between related items. This is a common requirement in business reporting, where analysts must compare performance across regions, products, or time periods. By selecting the right visualization, the analyst reduces the cognitive effort required for the audience to extract insights from the data.

Answer

Heatmap

Answer

Bubble Chart

Answer

Stacked Area Chart

Question 10

A data analyst needs to ingest a daily batch of comma-separated value (CSV) files stored in cloud object storage into a Databricks Delta table. The files contain a header row, use a semicolon (;) as the delimiter, and occasionally include embedded newlines within quoted fields. Which approach ensures the parser correctly handles the multi-line fields and missing values?

Accepted Answer

Apply the spark.read.format("csv").option("delimiter", ";").option("header", "true").option("multiLine", "true").load(path) command.. Configuring the Spark CSV reader with explicit options like multiLine set to true ensures that records containing embedded newlines are not split prematurely across rows. Data analysts frequently encounter messy raw external datasets where standard single-line parsing fails, leading to corrupted row counts and schema mismatches. Explicitly defining formatting options guarantees reliable ingestion before downstream business intelligence reporting takes place.

Answer

Use spark.read.table() with default parameters because Spark automatically infers delimiters and multiline configurations for all incoming CSV files.

Answer

Convert the CSV files into Parquet format using a local Python script outside Databricks before uploading them to the workspace file system.

Answer

Execute a standard SQL COPY INTO command without specifying any options, as Delta tables automatically resolve non-standard delimiters.