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The Databricks Certified Data Engineer Associate Exam certification exam is designed for data engineers, data analysts, and data scientists who build and maintain data pipelines using Databricks. Databricks-Certified-Data-Engineer-Associate exam is intended to validate an individual’s skills in data engineering and Databricks, enabling them to design and implement efficient data pipelines. Databricks Certified Data Engineer Associate Exam certification exam aims to test the candidate’s knowledge of data processing and data modeling, as well as their ability to use Databricks to process large datasets.

NO.66 A data engineer wants to schedule their Databricks SQL dashboard to refresh every hour, but they only want the associated SQL endpoint to be running when it is necessary. The dashboard has multiple queries on multiple datasets associated with it. The data that feeds the dashboard is automatically processed using a Databricks Job.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

They can turn on the Auto Stop feature for the SQL endpoint.

They can ensure the dashboard’s SQL endpoint is not one of the included query’s SQL endpoint.

They can reduce the cluster size of the SQL endpoint.

They can ensure the dashboard’s SQL endpoint matches each of the queries’ SQL endpoints.

They can set up the dashboard’s SQL endpoint to be serverless.

The Auto Stop feature allows the SQL endpoint to automatically stop after a specified period of inactivity. This can help reduce the cost and resource consumption of the SQL endpoint, as it will only run when it is needed to refresh the dashboard or execute queries. The data engineer can configure the Auto Stop setting for the SQL endpoint from the SQL Endpoints UI, by selecting the desired idle time from the Auto Stop dropdown menu. The default idle time is 120 minutes, but it can be set to as low as 15 minutes or as high as 240 minutes. Alternatively, the data engineer can also use the SQL Endpoints REST API to set the Auto Stop setting programmatically. Reference: SQL Endpoints UI, SQL Endpoints REST API, Refreshing SQL Dashboard

NO.67 A dataset has been defined using Delta Live Tables and includes an expectations clause:
CONSTRAINT valid_timestamp EXPECT (timestamp > ‘2020-01-01’) ON VIOLATION DROP ROW What is the expected behavior when a batch of data containing data that violates these constraints is processed?

Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.

Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.

Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.

Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.

Records that violate the expectation cause the job to fail.

Erläuterung
With the defined constraint and expectation clause, when a batch of data is processed, any records that violate the expectation (in this case, where the timestamp is not greater than ‘2020-01-01’) will be dropped from the target dataset. These dropped records will also be recorded as invalid in the event log, allowing for auditing and tracking of the data quality issues without causing the entire job to fail.
https://docs.databricks.com/en/delta-live-tables/expectations.html

NR. 68 What Databricks feature can be used to check the data sources and tables used in a workspace?

Do not use the lineage feature as it only tracks activity from the last 3 months and will not provide full details on dependencies.

Use the lineage feature to visualize a graph that highlights where the table is used only in notebooks,

Use the lineage feature to visualize a graph that highlights where the table is used only in reports.

Use the lineage feature to visualize a graph that shows all dependencies, including where the table is used in notebooks, other tables, and reports.

NR. 69 A data engineer needs access to a table new_table, but they do not have the correct permissions. They can ask the table owner for permission, but they do not know who the table owner is.
Which of the following approaches can be used to identify the owner of new_table?

Review the Permissions tab in the table’s page in Data Explorer

All of these options can be used to identify the owner of the table

Review the Owner field in the table’s page in Data Explorer

Review the Owner field in the table’s page in the cloud storage solution

There is no way to identify the owner of the table

he approach that can be used to identify the owner of new_table is to review the Owner field in the table’s page in Data Explorer. Data Explorer is a web-based interface that allows users to browse, create, and manage data objects such as tables, views, and functions in Databricks1. The table’s page in Data Explorer provides various information about the table, such as its schema, partitions, statistics, history, and permissions2. The Owner field shows the name and email address of the user who created or owns the table3. The data engineer can use this information to contact the table owner and request for permission to access the table.
The other options are not correct or reliable for identifying the owner of new_table. Reviewing the Permissions tab in the table’s page in Data Explorer can show the users and groups who have access to the table, but not necessarily the owner4. Reviewing the Owner field in the table’s page in the cloud storage solution can be misleading, as the owner of the data files may not be the same as the owner of the table5.
There is a way to identify the owner of the table, as explained above, so option E is false.
:
1: Data Explorer | Databricks on AWS
2: Table details | Databricks on AWS
3: Set owner when creating a view in databricks sql – Databricks – 9978
4: Table access control | Databricks on AWS
5: External tables | Databricks on AWS

NR. 70 A data engineer needs to apply custom logic to string column city in table stores for a specific use case. In order to apply this custom logic at scale, the data engineer wants to create a SQL user-defined function (UDF).
Which of the following code blocks creates this SQL UDF?

NR. 71 A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?

https://www.w3schools.com/python/python_functions.asp
https://www.geeksforgeeks.org/python-functions/

NR. 72 A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task.
Which of the following approaches can the data engineer use to set up the new task?

They can clone the existing task in the existing Job and update it to run the new notebook.

They can create a new task in the existing Job and then add it as a dependency of the original task.

They can create a new task in the existing Job and then add the original task as a dependency of the new task.

They can create a new job from scratch and add both tasks to run concurrently.

They can clone the existing task to a new Job and then edit it to run the new notebook.

To set up the new task to run a new notebook prior to the original task in a single-task Job, the data engineer can use the following approach: In the existing Job, create a new task that corresponds to the new notebook that needs to be run. Set up the new task with the appropriate configuration, specifying the notebook to be executed and any necessary parameters or dependencies. Once the new task is created, designate it as a dependency of the original task in the Job configuration. This ensures that the new task is executed before the original task.

NR. 73 A data engineer needs to ingest from both streaming and batch sources for a firm that relies on highly accurate data. Occasionally, some of the data picked up by the sensors that provide a streaming input are outside the expected parameters. If this occurs, the data must be dropped, but the stream should not fail.
Which feature of Delta Live Tables meets this requirement?

Überwachung

Change Data Capture

Expectations

Error Handling

NO.74 Which of the following data workloads will utilize a Gold table as its source?

A job that enriches data by parsing its timestamps into a human-readable format

A job that aggregates uncleaned data to create standard summary statistics

A job that cleans data by removing malformatted records

A job that queries aggregated data designed to feed into a dashboard

A job that ingests raw data from a streaming source into the Lakehouse

NO.75 A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Development mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.

All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.

All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.

All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.

The Continuous Pipeline Mode for Delta Live Tables allows the pipeline to run continuously and process data as it arrives. This mode is suitable for streaming ingest and CDC workloads that require low-latency updates.
The Development mode for Delta Live Tables allows the pipeline to run on a dedicated cluster that is not shared with other pipelines. This mode is useful for testing and debugging the pipeline logic before deploying it to production. Therefore, the correct answer is B, because the pipeline will run continuously on a dedicated cluster until it is manually stopped, and the compute resources will be released only after the pipeline is shut down. References: Databricks Documentation – Configure pipeline settings for Delta Live Tables, Databricks Documentation – Continuous vs. triggered pipeline execution, Databricks Documentation – Development vs.
production mode.

NR. 76 A data engineer is maintaining a data pipeline. Upon data ingestion, the data engineer notices that the source data is starting to have a lower level of quality. The data engineer would like to automate the process of monitoring the quality level.
Which of the following tools can the data engineer use to solve this problem?

Unity Catalog

Data Explorer

Delta Lake

Delta Live Tables

Auto Loader

Delta Live Tables is a tool that enables data engineers to build and manage reliable data pipelines with minimal code. One of the features of Delta Live Tables is data quality monitoring, which allows data engineers to define quality expectations for their data and automatically check them at every step of the pipeline. Data quality monitoring can help detect and resolve data quality issues, such as missing values, duplicates, outliers, or schema changes. Data quality monitoring can also generate alerts and reports on the quality level of the data, and enable data engineers to troubleshoot and fix problems quickly. References: Delta Live Tables Overview, Data Quality Monitoring

NR. 77 Which of the following describes the relationship between Gold tables and Silver tables?

Gold tables are more likely to contain aggregations than Silver tables.

Gold tables are more likely to contain valuable data than Silver tables.

Gold tables are more likely to contain a less refined view of data than Silver tables.

Gold tables are more likely to contain more data than Silver tables.

Gold tables are more likely to contain truthful data than Silver tables.

According to the medallion lakehouse architecture, gold tables are the final layer of data that powers analytics, machine learning, and production applications. They are often highly refined and aggregated, containing data that has been transformed into knowledge, rather than just information. Silver tables, on the other hand, are the intermediate layer of data that represents a validated, enriched version of the raw data from the bronze layer.
They provide an enterprise view of all its key business entities, concepts and transactions, but they may not have all the aggregations and calculations that are required for specific use cases. Therefore, gold tables are more likely to contain aggregations than silver tables. References:
* What is the medallion lakehouse architecture?
* What is a Medallion Architecture?

NO.78 Which of the following Git operations must be performed outside of Databricks Repos?

Commit

Ziehen Sie

Schieben Sie

Klonen

Zusammenführen

NO.79 A data engineering team has two tables. The first table march_transactions is a collection of all retail transactions in the month of March. The second table april_transactions is a collection of all retail transactions in the month of April. There are no duplicate records between the tables.
Which of the following commands should be run to create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records?

CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INNER JOIN SELECT * FROM april_transactions;

CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
UNION SELECT * FROM april_transactions;

CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
OUTER JOIN SELECT * FROM april_transactions;

CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
INTERSECT SELECT * from april_transactions;

CREATE TABLE all_transactions AS
SELECT * FROM march_transactions
MERGE SELECT * FROM april_transactions;

The correct command to create a new table that contains all records from two tables without duplicate records is to use the UNION operator. The UNION operator combines the results of two queries and removes any duplicate rows. The INNER JOIN, OUTER JOIN, and MERGE operators do not remove duplicate rows, and the INTERSECT operator only returns the rows that are common to both tables. Therefore, option B is the only correct answer. References: Databricks SQL Reference – UNION, Databricks SQL Reference – JOIN, Databricks SQL Reference – MERGE, [Databricks SQL Reference – INTERSECT]

NO.80 A data engineer is attempting to drop a Spark SQL table my_table. The data engineer wants to delete all table metadata and data.
They run the following command:
DROP TABLE IF EXISTS my_table
While the object no longer appears when they run SHOW TABLES, the data files still exist.
Which of the following describes why the data files still exist and the metadata files were deleted?

The table’s data was larger than 10 GB

The table’s data was smaller than 10 GB

The table was external

The table did not have a location

The table was managed

An external table is a table that is defined in the metastore and points to an existing location in the storage system. When you drop an external table, only the metadata is deleted from the metastore, but the data files are not deleted from the storage system. This is because external tables are meant to be shared by multiple applications and users, and dropping them should not affect the data availability. On the other hand, a managed table is a table that is defined in the metastore and also managed by the metastore. When you drop a managed table, both the metadata and the data files are deleted from the metastore and the storage system, respectively. This is because managed tables are meant to be exclusive to the application or user that created them, and dropping them should free up the storage space. Therefore, the correct answer is C, because the table was external and only the metadata was deleted when the table was dropped. References: Databricks Documentation – Managed and External Tables, Databricks Documentation – Drop Table

NR. 81 A data engineer needs to create a table in Databricks using data from their organization’s existing SQLite database.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

org.apache.spark.sql.jdbc

autoloader

DELTA

sqlite

org.apache.spark.sql.sqlite

Erläuterung
CREATE TABLE new_employees_table
USING JDBC
OPTIONS (
url “<jdbc_url>”,
dbtable “<table_name>”,
user ‘<username>’,
password ‘<password>’
) AS
SELECT * FROM employees_table_vw
https://docs.databricks.com/external-data/jdbc.html#language-sql

NR. 82 A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The cade block used by the data engineer is below:

If the data engineer only wants the query to execute a micro-batch to process data every 5 seconds, which of the following lines of code should the data engineer use to fill in the blank?

trigger(“5 seconds”)

trigger()

trigger(once=”5 seconds”)

trigger(processingTime=”5 seconds”)

trigger(continuous=”5 seconds”)

The processingTime option specifies a time-based trigger interval for fixed interval micro-batches. This means that the query will execute a micro-batch to process data every 5 seconds, regardless of how much data is available. This option is suitable for near-real time processing workloads that require low latency and consistent processing frequency. The other options are either invalid syntax (A, C), default behavior (B), or experimental feature (E). References: Databricks Documentation – Configure Structured Streaming trigger intervals, Databricks Documentation – Trigger.

NR. 83 A data engineer has joined an existing project and they see the following query in the project repository:
CREATE STREAMING LIVE TABLE loyal_customers AS
SELECT customer_id –
FROM STREAM(LIVE.customers)
WHERE loyalty_level = ‘high’;
Which of the following describes why the STREAM function is included in the query?

The STREAM function is not needed and will cause an error.

The table being created is a live table.

The customers table is a streaming live table.

The customers table is a reference to a Structured Streaming query on a PySpark DataFrame.

The data in the customers table has been updated since its last run.

The STREAM function is used to process data from a streaming live table or view, which is a table or view that contains data that has been added only since the last pipeline update. Streaming live tables and views are stateful, meaning that they retain the state of the previous pipeline run and only process new data based on the current query. This is useful for incremental processing of streaming or batch data sources. The customers table in the query is a streaming live table, which means that it contains the latest data from the source. The STREAM function enables the query to read the data from the customers table incrementally and create another streaming live table named loyal_customers, which contains the customer IDs of the customers with high loyalty level. References: Difference between LIVE TABLE and STREAMING LIVE TABLE, CREATE STREAMING TABLE, Load data using streaming tables in Databricks SQL.

Laden …

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