What is an advantage of using Databricks Repos over the built-in versioning feature of Databricks Notebooks?

Repos support multiple branches for collaborative development

Repos automatically save notebook progress in real time

Repos allow reverting to earlier notebook versions

Repos provide threaded comments directly within the notebook

Repos eliminate the need for Git integration when tracking changes

Which of the following individuals is required to transfer ownership of Delta tables if the current owner has left the company?

A Databricks account representative

Ownership cannot be transferred after the owner leaves

The new team lead or data owner

Any user with edit permissions on the table

Which SQL command will return the storage location of the marketing database?

DESCRIBE DATABASE EXTENDED marketing;

SHOW LOCATION FOR DATABASE marketing;

SHOW DATABASE EXTENDED LIKE 'marketing';

SELECT * FROM marketing.INFORMATION;

A data engineer wants to create a new Delta table using a CTAS (Create Table As Select) statement. They also want to document the purpose of the table by adding a comment. Review the code below: CREATE TABLE top_customers ---- AS SELECT customer_id, SUM(sales_amount) AS total_sales FROM sales_data GROUP BY customer_id HAVING total_sales > 10000; Which line of code should be added in the comment section to properly document the table as "Contains high-value customers based on total sales"?

COMMENT 'Contains high-value customers based on total sales'

COMMENT = 'Contains high-value customers based on total sales'

COMMENT ON TABLE top_customers IS 'Contains high-value customers based on total sales'

COMMENT: Contains high-value customers based on total sales

SET COMMENT = 'Contains high-value customers based on total sales'

Array functions in Spark SQL are primarily used for which of the following purposes?

Transforming and querying nested or repeated data structures

Managing relational keys across multiple tables

Creating partitions for large-scale parallel processing

Scheduling jobs based on array-based parameters

Joining multiple tables using array-like syntax

Which of the following SQL code snippets correctly creates a SQL User-Defined Function (UDF)?

CREATE FUNCTION TO_UPPER(s STRING) RETURNS STRING RETURN UPPER(s);

DEFINE FUNCTION TO_UPPER(s STRING) AS UPPER(s);

CREATE TEMPORARY FUNCTION TO_UPPER AS 'org.apache.spark.sql.functions.upper';

CREATE OR REPLACE FUNCTION TO_UPPER AS (s STRING) -> UPPER(s);

REGISTER FUNCTION TO_UPPER USING UPPER();

A data engineer runs the following command to load data into a Delta table: COPY INTO my_table FROM '/path/to/files' FILEFORMAT = PARQUET; However, no new data is loaded into the table. What is the most likely explanation?

The files were already ingested in a previous COPY INTO operation

The file format was not configured using the FORMAT_OPTIONS clause

The specific file names to be loaded were not explicitly listed using the FILES clause

The PARQUET file format is incompatible with the COPY INTO command

The data will not be visible until the table is refreshed using a separate command

A data engineer is working with two Delta tables: north_region_sales and south_region_sales, each containing unique sales records from two different regions. The engineer wants to combine these tables into a new table called total_sales, ensuring that all records are included and no duplicates are introduced. Which of the following commands should they use?

CREATE TABLE total_sales AS SELECT * FROM north_region_sales UNION SELECT * FROM south_region_sales;

CREATE TABLE total_sales AS SELECT * FROM north_region_sales INTERSECT SELECT * FROM south_region_sales;

CREATE TABLE total_sales AS SELECT * FROM north_region_sales UNION ALL SELECT * FROM south_region_sales;

CREATE TABLE total_sales AS MERGE INTO north_region_sales USING south_region_sales;

A data scientist is developing a Python script to trigger a model retraining job only if it's Monday (day_of_week == 1) and the retraining_flag is set to true. Which of the following lines of code correctly checks both conditions?

if day_of_week == 1 and retraining_flag:

if day_of_week = 1 and retraining_flag:

if day_of_week = 1 and retraining_flag == "True":

if day_of_week == 1 and retraining_flag == "True":

if day_of_week = 1 & retraining_flag: = "True":

A data analyst is cleaning up old project assets and executes the following command to remove a table named archived_customers: DROP TABLE IF EXISTS archived_customers; After running the command, they verify that the table no longer appears in the database using SHOW TABLES. However, upon checking the storage location, they notice that the actual data files are still present in the directory. What is the most likely reason the data files were not deleted along with the table metadata?

The table was linked to external storage, so its files are not managed by Spark

The dataset was too large for automatic deletion of files

The dataset was too small to trigger a full cleanup

The table was missing a specified schema or location

The table was registered as a fully managed asset and should have been deleted entirely

A data engineer needs to create a data object by joining multiple tables. The object must be stored physically and accessible in different sessions.

A table, which stores data physically and can be shared across sessions

A database, to organize multiple tables logically

A function, to encapsulate reusable transformation logic

A view, to define a logical representation without storing physical data

A temporary view, which is only accessible within the current session

Which of the following mechanisms are used in Structured Streaming to ensure fault tolerance and recovery in the event of a failure?

Checkpointing and Write-ahead Logs

Inability to track offset ranges during each processing trigger

Use of data sources that can be replayed and sinks that avoid duplicate writes

Logging all writes in advance and using sinks that discard duplicates

Saving state and using sinks that prevent reprocessing errors

Which of the following best describes the relationship between Gold tables and Silver tables in a data lakehouse architecture?

Gold tables refine data further by summarizing and aggregating cleansed data from Silver tables

Gold tables store raw data, while Silver tables store aggregated metrics

Gold tables are used for real-time ingestion, and Silver tables are used for batch loads

Gold tables contain duplicate data from Silver tables to ensure redundancy

Gold tables and Silver tables are two names for the same layer of data

Which of the following best describes the relationship between Bronze tables and raw data in a data lakehouse architecture?

Bronze tables are used to store raw or ingested data with minimal transformation

Bronze tables store the final aggregated data used for reporting

Bronze tables are a backup of cleaned Silver data

Bronze tables contain only metadata about raw files, not actual data

Bronze tables are temporary views created during real-time processing

Which of the following best describes how Auto Loader processes data in Databricks?

It incrementally processes new files by maintaining state information and file metadata

It loads all files from the source location every time the job runs

It requires manual tracking of processed files for incremental loads

It only supports batch ingestion, not streaming

It uses database triggers to detect new files for ingestion

A data engineer is setting up a Structured Streaming job in PySpark and wants it to run every 5 seconds. Review the code below: streamingDF = spark.readStream.format("csv") \ .option("header", "true") \ .schema(schema) \ .load("/path/to/input") query = streamingDF.writeStream \ .format("console") \ .outputMode("append") \ .----- # Fill in trigger here .start() Which of the following options should be used to configure the stream to process data every 5 seconds?

trigger(processingTime="5 seconds")

trigger(continuous="5 seconds")

A data engineer defines an expectation in a Delta Live Table pipeline to enforce that all values in the sales_amount column must be greater than 0. They use the following clause: EXPECT (sales_amount > 0) ON VIOLATION DROP ROW What will happen when a batch of data containing rows with sales_amount <= 0 is processed?

Rows violating the condition will be skipped, and the rest will be written

The pipeline will fail, and no rows will be written

All data in the batch will be ignored

Only the rows violating the expectation will be written

A warning will be logged, but all rows will be written

A data engineer sets up an expectation in a DLT pipeline with the following clause: EXPECT (order_date IS NOT NULL) ON VIOLATION FAIL UPDATE What will be the outcome if the incoming batch contains one or more rows where order_date is NULL?

The pipeline will fail, and no rows in the batch will be written

Only the valid rows will be processed, and invalid rows will be dropped

The pipeline will continue processing and log the issue

A NULL value will be replaced with the current date

The invalid rows will be moved to a quarantine table

A data engineer is working with streaming data from a Silver table and wants to continuously write aggregated results to a Gold table using PySpark. Which of the following PySpark code snippets correctly reads from a Silver Delta table and writes a running total to a Gold Delta table using structured streaming?

spark.readStream.table("sales_new") \ .groupBy("customer_id").sum("sales_amount") \ .writeStream \ .format("delta") \ .outputMode("complete") \ .option("checkpointLocation", "/checkpoints/sales_data") \ .table("sales_data")

spark.readStream.format("delta").load("sales_new") \ .groupBy("customer_id").sum("sales_amount") \ .writeStream \ .format("delta") \ .outputMode("complete") \ .start("/gold/sales_data")

spark.read.table("sales_new") \ .groupBy("customer_id").sum("sales_amount") \ .writeStream \ .format("parquet") \ .start("/gold/sales_data")

spark.readStream.table("sales_new") \ .groupBy("customer_id").sum("sales_amount") \ .writeStream \ .outputMode("append") \ .option("checkpointLocation", "/checkpoints/sales_data") \ .table("sales_data")

spark.readStream.format("delta").table("sales_new") \ .agg({"sales_amount": "sum"}) \ .writeStream \ .format("console") \ .start()

A data engineer is managing a Delta Live Tables (DLT) pipeline that includes multiple transformation steps: ingesting raw data, cleaning it, and producing a refined output. They have applied data quality expectations with ON VIOLATION DROP ROW at different stages of the pipeline to remove invalid records. Now, they notice that some expected records are missing in the final output and suspect that one of the intermediate tables is dropping them due to data quality checks. How can the data engineer identify which stage of the pipeline is dropping the rows?

Use the DLT UI to inspect each table and review row-level quality metrics

Check the pipeline logs from the cluster event timeline for error messages

Compare row counts across all tables using external BI tools

Analyze job run metrics in the Spark UI for dropped data indicators

Use Auto Loader history logs to trace the origin of dropped records

A data engineer has a Databricks Job that trains a machine learning model on daily sales data. After a recent request from the analytics team, they need to generate summary statistics before model training begins. This new preprocessing task must execute first and only then trigger the model training task. Which of the following is the correct way to set up this workflow in Databricks?

They can add a new task to the current job for summary generation and set the model training task to depend on it

They can insert the summary code into the model training notebook and run everything in a single task

They can create a new job that contains only the summary task and run it manually before the model training job

They can split the existing job into two separate jobs and schedule them back-to-back

They can modify the existing task to run both notebooks in parallel using multitasking

An organization has created a new group called data_eng_team in Unity Catalog to manage all ingestion, transformation, and validation tasks for the sales_data database. To allow them to fully manage schemas, tables, and permissions within that database, the administrator needs to assign full access rights. Which of the following SQL commands should be used to grant complete privileges on the sales_data database to the data_eng_team group?

GRANT ALL PRIVILEGES ON DATABASE sales_data TO data_eng_team;

GRANT USAGE ON DATABASE sales_data TO data_eng_team;

GRANT ALL PRIVILEGES ON DATABASE data_eng_team TO sales_data;

GRANT SELECT ON DATABASE sales_data TO data_eng_teams;

GRANT USAGE, CREATE, SELECT ON DATABASE sales_data TO data_eng_team;

A data engineer needs to append a new product record to the existing Delta table named products. The new record contains the following values: product_id STRING = 'P100' category_id INT = 12 price FLOAT = 29.99 Which of the following SQL commands can the data engineer use to append this new record to the products table?

INSERT INTO products VALUES ('P100', 12, 29.99)

products UNION VALUES ('P100', 12, 29.99)

INSERT VALUES ('P100', 12, 29.99) INTO products

UPDATE products SET VALUES ('P100', 12, 29.99)

MERGE INTO products VALUES ('P100', 12, 29.99)

A data engineer is working with a Delta table named projects, where each row contains an array column contributors, storing contributor objects with fields name and commits. The engineer wants to add a new column active_contributors that includes only those contributors with more than 50 commits. To apply this transformation efficiently across the dataset, the data engineer decides to use the FILTER higher-order function. Which of the following code blocks successfully creates the active_contributors column using the desired logic?

SELECT *, FILTER(contributors, c -> c.commits > 50) AS active_contributors FROM projects

SELECT *, MAP(contributors, c -> c.commits > 50) AS active_contributors FROM projects

SELECT *, TRANSFORM(contributors, c -> c.commits > 50) AS active_contributors FROM projects

SELECT *, FILTER(c -> c.commits > 50 IN contributors) AS active_contributors FROM projects

SELECT *, WHERE contributors.commits > 50 AS active_contributors FROM projects

A data engineer runs the following command in a Databricks workspace to create a new database for reporting purposes: CREATE DATABASE IF NOT EXISTS sales_reporting; The engineer does not specify a custom location in the command. Where will the sales_reporting database be stored by default?

dbfs:/user/hive/warehouse/sales_reporting

dbfs:/user/hive/database/sales_reporting

dbfs:/user/hive/sales_reporting

More information is needed to determine the correct response

A data engineer is setting up a new Delta Live Tables (DLT) pipeline in Databricks. They are reviewing the minimum requirements for pipeline creation. Which of the following must be specified when creating a new Delta Live Tables pipeline?

At least one notebook or Python script to be executed

A path to cloud storage location for the written data

A location of a target database for the written data

A data engineer is creating a streaming live table using the following SQL command: CREATE STREAMING LIVE TABLE high_value_orders AS SELECT order_id, total_amount FROM STREAM(LIVE.orders) WHERE total_amount > 1000; Which of the following best explains why the STREAM function is used in this query?

The orders table is a streaming live table and needs to be consumed as a stream.

The STREAM function is optional and should be removed for better performance.

The orders table is a static input table and must be converted to streaming.

The query is not valid unless the source is an external Kafka stream.

The STREAM function is required when filtering on numeric columns.

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MULTIPLE CHOICE QUESTION

45 sec • 1 pt

Isolated data systems can cause inconsistencies between teams, such as differing reports from the data analysis and data engineering teams. Which of the following best describes how a data lakehouse can help address this issue?

By allowing both teams to automatically scale their workloads as data volume increases

By enabling both teams to work from a unified and consistent source of truth

By requiring both teams to operate under the same department or management structure

By allowing both teams to collaborate on data projects in real time

MULTIPLE CHOICE QUESTION

45 sec • 1 pt

Cluster pools will be utilized in which of the following scenarios?

When an automated report needs to refresh as quickly as possible

When an automated report needs to be reproducible

When an automated report needs to be tested for errors

When an automated report needs to be accessible to all stakeholders

MULTIPLE CHOICE QUESTION

45 sec • 1 pt

Which of the following is a key benefit of using Delta Lake?

Real-time notebook collaboration

Query failure alerts

Unified batch and streaming support

Distributed data processing

MULTIPLE CHOICE QUESTION

45 sec • 1 pt

How is data organized in storage when using Delta Lake?

As raw files without any metadata

As unstructured blobs optimized for speed

As Parquet files with a transaction log for ACID compliance

As JSON files grouped by ingestion time

As key-value pairs stored in memory

MULTIPLE CHOICE QUESTION

45 sec • 1 pt

Which SQL query will remove rows from a Delta table where the sales_amount is less than equal to 25?

DELETE FROM delta_table WHERE sales_amount <= 25;

REMOVE FROM delta_table WHERE sales_amount < 25;

DROP ROWS FROM delta_table WHERE sales_amount <= 25;

DELETE ROW FROM delta_table WHERE sales_amount <= 25;

TRUNCATE delta_table WHERE sales_amount < 25;

MULTIPLE CHOICE QUESTION

45 sec • 1 pt

Which of the following actions would result in the removal of old files no longer referenced by the Delta table?

The VACUUM command was run on the table

The DELETE HISTORY command was run on the table

The OPTIMIZE command was run on the table

The HISTORY command was run on the table

MULTIPLE CHOICE QUESTION

45 sec • 1 pt

Which of the following best highlights a core advantage of using a data lakehouse architecture?

It can store both structured and unstructured data efficiently

It supports ACID-compliant transactions to ensure reliable and consistent data operations

It enables querying data using SQL

It uses open file formats to ensure compatibility

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