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The GAQM Databricks-Certified-Data-Engineer-Associate certification exam is designed to test the skills and knowledge of data engineers who use Databricks for data processing and analysis. Databricks is a cloud-based data engineering platform that provides an integrated environment for data engineers to collaborate and build data pipelines. Databricks Certified Data Engineer Associate Exam certification exam covers a range of topics related to Databricks, including data ingestion, data transformation, data processing, and data analysis.
NEW QUESTION # 54
Which of the following Git operations must be performed outside of Databricks Repos?
- A. Commit
- B. Push
- C. Merge
- D. Pull
- E. Clone
Answer: C
Explanation:
Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as commit, pull, push, branch management, and visual comparison of diffs when committing1. However, merge is not supported in the Git dialog2. You need to use the Repos UI or your Git provider to merge branches3. Merge is a way to combine the commit history from one branch into another branch1. During a merge, a merge conflict is encountered when Git cannot automatically combine code from one branch into another. Merge conflicts require manual resolution before a merge can be completed1. References: 4: Run Git operations on Databricks Repos4, 1: CI/CD techniques with Git and Databricks Repos1, 3: Collaborate in Repos3, 2: Databricks Repos - What it is and how we can use it2.
Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as commit, pull, push, merge, and branch management. However, to clone a remote Git repository to a Databricks repo, you must use the Databricks UI or API. You cannot clone a Git repo using the CLI through a cluster's web terminal, as the files won't display in the Databricks UI1. References: 1: Run Git operations on Databricks Repos | Databricks on AWS2
NEW QUESTION # 55
A data engineer has a Python variable table_name that they would like to use in a SQL query. They want to construct a Python code block that will run the query using table_name.
They have the following incomplete code block:
____(f"SELECT customer_id, spend FROM {table_name}")
Which of the following can be used to fill in the blank to successfully complete the task?
- A. spark.table
- B. dbutils.sql
- C. spark.sql
- D. spark.delta.table
- E. spark.delta.sql
Answer: C
NEW QUESTION # 56
A data engineer has a Job that has a complex run schedule, and they want to transfer that schedule to other Jobs.
Rather than manually selecting each value in the scheduling form in Databricks, which of the following tools can the data engineer use to represent and submit the schedule programmatically?
- A. datetime
- B. pyspark.sql.types.TimestampType
- C. pyspark.sql.types.DateType
- D. Cron syntax
- E. There is no way to represent and submit this information programmatically
Answer: D
Explanation:
Cron syntax is a tool that can be used to represent and submit a complex run schedule programmatically. Cron syntax is a string of six fields that specify the frequency, date, and time of a job run. For example, the cron expression 0 0 12 * * ? means run the job at 12:00 PM every day. The data engineer can use the Databricks REST API to create or update a job with a cron schedule. The data engineer can also use the Databricks CLI to create or update a job with a cron schedule by using a JSON file that contains the cron expression. The other tools are either invalid or not suitable for representing and submitting a complex run schedule programmatically. Reference: Schedule a job, Jobs API, Databricks CLI, Cron expressions
NEW QUESTION # 57
Which of the following tools is used by Auto Loader process data incrementally?
- A. Checkpointing
- B. Databricks SQL
- C. Unity Catalog
- D. Data Explorer
- E. Spark Structured Streaming
Answer: E
NEW QUESTION # 58
A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?
- A. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized." - B. They can turn on the Auto Stop feature for the SQL endpoint.
- C. They can turn on the Serverless feature for the SQL endpoint.
- D. They can increase the cluster size of the SQL endpoint.
- E. They can increase the maximum bound of the SQL endpoint's scaling range
Answer: D
Explanation:
Explanation
https://www.databricks.com/blog/2022/03/10/top-5-databricks-performance-tips.html
NEW QUESTION # 59
A data engineer has been using a Databricks SQL dashboard to monitor the cleanliness of the input data to an ELT job. The ELT job has its Databricks SQL query that returns the number of input records containing unexpected NULL values. The data engineer wants their entire team to be notified via a messaging webhook whenever this value reaches 100.
Which of the following approaches can the data engineer use to notify their entire team via a messaging webhook whenever the number of NULL values reaches 100?
- A. They can set up an Alert with a custom template.
- B. They can set up an Alert with a new email alert destination.
- C. They can set up an Alert without notifications.
- D. They can set up an Alert with a new webhook alert destination.
- E. They can set up an Alert with one-time notifications.
Answer: D
Explanation:
A webhook alert destination is a way to send notifications to external applications or services via HTTP requests. A data engineer can use a webhook alert destination to notify their entire team via a messaging webhook, such as Slack or Microsoft Teams, whenever the number of NULL values in the input data reaches 100. To set up a webhook alert destination, the data engineer needs to do the following steps:
In the Databricks SQL workspace, navigate to the Settings gear icon and select SQL Admin Console.
Click Alert Destinations and click Add New Alert Destination.
Select Webhook and enter the webhook URL and the optional custom template for the notification message.
Click Create to save the webhook alert destination.
In the Databricks SQL editor, create or open the query that returns the number of input records containing unexpected NULL values.
Click the Create Alert icon above the editor window and configure the alert criteria, such as the value column, the condition, and the threshold.
In the Notification section, select the webhook alert destination that was created earlier and click Create Alert. Reference: What are Databricks SQL alerts?, Monitor alerts, Monitoring Your Business with Alerts, Using Automation Runbook Webhooks To Alert on Databricks Status Updates.
NEW QUESTION # 60
Which of the following commands will return the location of database customer360?
- A. USE DATABASE customer360;
- B. DROP DATABASE customer360;
- C. DESCRIBE DATABASE customer360;
- D. ALTER DATABASE customer360 SET DBPROPERTIES ('location' = '/user'};
- E. DESCRIBE LOCATION customer360;
Answer: C
NEW QUESTION # 61
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?
- A. The ability to support batch and streaming workloads
- B. The ability to set up alerts for query failures
- C. The ability to distribute complex data operations
- D. The ability to collaborate in real time on a single notebook
- E. The ability to manipulate the same data using a variety of languages
Answer: A
NEW QUESTION # 62
A data engineer has realized that the data files associated with a Delta table are incredibly small. They want to compact the small files to form larger files to improve performance.
Which of the following keywords can be used to compact the small files?
- A. OPTIMIZE
- B. VACUUM
- C. REPARTITION
- D. REDUCE
- E. COMPACTION
Answer: A
Explanation:
Explanation
OPTIMIZE can be used to club small files into 1 and improve performance.
NEW QUESTION # 63
A data organization leader is upset about the data analysis team's reports being different from the data engineering team's reports. The leader believes the siloed nature of their organization's data engineering and data analysis architectures is to blame.
Which of the following describes how a data lakehouse could alleviate this issue?
- A. Both teams would autoscale their work as data size evolves
- B. Both teams would be able to collaborate on projects in real-time
- C. Both teams would use the same source of truth for their work
- D. Both teams would respond more quickly to ad-hoc requests
- E. Both teams would reorganize to report to the same department
Answer: C
Explanation:
Explanation
A data lakehouse is designed to unify the data engineering and data analysis architectures by integrating features of both data lakes and data warehouses. One of the key benefits of a data lakehouse is that it provides a common, centralized data repository (the "lake") that serves as a single source of truth for data storage and analysis. This allows both data engineering and data analysis teams to work with the same consistent data sets, reducing discrepancies and ensuring that the reports generated by both teams are based on the same underlying data.
NEW QUESTION # 64
A data engineer is working with two tables. Each of these tables is displayed below in its entirety.
The data engineer runs the following query to join these tables together:
Which of the following will be returned by the above query?
- A. Option A
- B. Option C
- C. Option D
- D. Option B
- E. Option E
Answer: A
Explanation:
Option A is the correct answer because it shows the result of an INNER JOIN between the two tables. An INNER JOIN returns only the rows that have matching values in both tables based on the join condition. In this case, the join condition is ON a.customer_id = c.customer_id, which means that only the rows that have the same customer ID in both tables will be included in the output. The output will have four columns: customer_id, name, account_id, and overdraft_amt. The output will have four rows, corresponding to the four customers who have accounts in the account table.
NEW QUESTION # 65
Which of the following describes a scenario in which a data engineer will want to use a single-node cluster?
- A. When they are manually running reports with a large amount of data
- B. When they are running automated reports to be refreshed as quickly as possible
- C. When they are working with SQL within Databricks SQL
- D. When they are concerned about the ability to automatically scale with larger data
- E. When they are working interactively with a small amount of data
Answer: E
Explanation:
The scenario in which a data engineer will want to use a single-node cluster is when they are working interactively with a small amount of data. A single-node cluster is a cluster consisting of an Apache Spark driver and no Spark workers1. A single-node cluster supports Spark jobs and all Spark data sources, including Delta Lake1. A single-node cluster is helpful for single-node machine learning workloads that use Spark to load and save data, and for lightweight exploratory data analysis1. A single-node cluster can run Spark locally, spawn one executor thread per logical core in the cluster, and save all log output in the driver log1. A single-node cluster can be created by selecting the Single Node button when configuring a cluster1.
The other options are not suitable for using a single-node cluster. When running automated reports to be refreshed as quickly as possible, a data engineer will want to use a multi-node cluster that can scale up and down automatically based on the workload demand2. When working with SQL within Databricks SQL, a data engineer will want to use a SQL Endpoint that can execute SQL queries on a serverless pool or an existing cluster3. When concerned about the ability to automatically scale with larger data, a data engineer will want to use a multi-node cluster that can leverage the Databricks Lakehouse Platform and the Delta Engine to handle large-scale data processing efficiently and reliably4. When manually running reports with a large amount of data, a data engineer will want to use a multi-node cluster that can distribute the computation across multiple workers and leverage the Spark UI to monitor the performance and troubleshoot the issues.
Reference:
1: Single Node clusters | Databricks on AWS
2: Autoscaling | Databricks on AWS
3: SQL Endpoints | Databricks on AWS
4: Databricks Lakehouse Platform | Databricks on AWS
5: [Spark UI | Databricks on AWS]
NEW QUESTION # 66
A data architect has determined that a table of the following format is necessary:
Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above format regardless of whether a table already exists with this name?
- A. Option C
- B. Option D
- C. Option B
- D. Option E
- E. Option A
Answer: D
Explanation:
References: Create a table using SQL | Databricks on AWS, Create a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks
NEW QUESTION # 67
A data engineer has left the organization. The data team needs to transfer ownership of the data engineer's Delta tables to a new data engineer. The new data engineer is the lead engineer on the data team.
Assuming the original data engineer no longer has access, which of the following individuals must be the one to transfer ownership of the Delta tables in Data Explorer?
- A. Workspace administrator
- B. New lead data engineer
- C. This transfer is not possible
- D. Databricks account representative
- E. Original data engineer
Answer: A
Explanation:
The workspace administrator is the only individual who can transfer ownership of the Delta tables in Data Explorer, assuming the original data engineer no longer has access. The workspace administrator has the highest level of permissions in the workspace and can manage all resources, users, and groups. The other options are either not possible or not sufficient to perform the ownership transfer. The Databricks account representative is not involved in the workspace management. The transfer is possible and not dependent on the original data engineer. The new lead data engineer may not have the necessary permissions to access or modify the Delta tables, unless granted by the workspace administrator or the original data engineer before leaving. References: Workspace access control, Manage Unity Catalog object ownership.
NEW QUESTION # 68
A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?
- A. Databricks Repos is wholly housed within the Databricks Lakehouse Platform
- B. Databricks Repos supports the use of multiple branches
- C. Databricks Repos provides the ability to comment on specific changes
- D. Databricks Repos automatically saves development progress
- E. Databricks Repos allows users to revert to previous versions of a notebook
Answer: B
Explanation:
Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as cloning, committing, pushing, pulling, and branch management. Databricks Notebooks versioning is a legacy feature that allows users to link notebooks to GitHub repositories and perform basic Git operations. However, Databricks Notebooks versioning does not support the use of multiple branches for development work, which is an advantage of using Databricks Repos. With Databricks Repos, users can create and manage branches for different features, experiments, or bug fixes, and merge, rebase, or resolve conflicts between them. Databricks recommends using a separate branch for each notebook and following data science and engineering code development best practices using Git for version control, collaboration, and CI/CD. References: Git integration with Databricks Repos - Azure Databricks | Microsoft Learn, Git version control for notebooks (legacy) | Databricks on AWS, Databricks Repos Is Now Generally Available - New 'Files' Feature in ..., Databricks Repos - What it is and how we can use it | Adatis.
NEW QUESTION # 69
A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?
- A. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to
"Reliability Optimized." - B. They can increase the cluster size of the SQL endpoint.
- C. They can turn on the Auto Stop feature for the SQL endpoint.
- D. They can increase the maximum bound of the SQL endpoint's scaling range
- E. They can turn on the Serverless feature for the SQL endpoint.
Answer: E
Explanation:
Option D is the correct answer because it enables the Serverless feature for the SQL endpoint, which allows the endpoint to automatically scale up and down based on the query load. This way, the endpoint can handle more concurrent queries and reduce the time it takes to return results. The Serverless feature also reduces the cold start time of the endpoint, which is the time it takes to start the cluster when a query is submitted to a non-running endpoint. The Serverless feature is available for both AWS and Azure Databricks platforms.
References: Databricks SQL Serverless, Serverless SQL endpoints, New Performance Improvements in Databricks SQL
NEW QUESTION # 70
A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?
- A. The HISTORY command was run on the table
- B. The TIME TRAVEL command was run on the table
- C. The OPTIMIZE command was nun on the table
- D. The VACUUM command was run on the table
- E. The DELETE HISTORY command was run on the table
Answer: D
Explanation:
Explanation
The VACUUM command in Delta Lake is used to clean up and remove unnecessary data files that are no longer needed for time travel or query purposes. When you run VACUUMwith certain retention settings, it can delete older data files, which might include versions of data that are older than the specified retention period. If the data engineer is unable to restore the table to a version that is 3 days old because the data files have been deleted, it's likely because the VACUUM command was run on the table, removing the older data files as part of data cleanup.
NEW QUESTION # 71
A data engineer wants to create a relational object by pulling data from two tables. The relational object does not need to be used by other data engineers in other sessions. In order to save on storage costs, the data engineer wants to avoid copying and storing physical data.
Which of the following relational objects should the data engineer create?
- A. Database
- B. View
- C. Delta Table
- D. Temporary view
- E. Spark SQL Table
Answer: D
Explanation:
Explanation
Temp view : session based Create temp view view_name as query All these are termed as session ended:
Opening a new notebook Detaching and reattaching a cluster Installing a python package Restarting a cluster
NEW QUESTION # 72
What is stored in a Databricks customer's cloud account?
- A. Data
- B. Cluster management metadata
- C. Databricks web application
- D. Notebooks
Answer: A
Explanation:
In a Databricks customer's cloud account, the primary elements stored include:
Data: This is the central type of content stored in the customer's cloud account. Data might include various datasets, tables, and files that are used and managed through Databricks platforms.
Notebooks: These are also stored within a customer's cloud account. Notebooks include scripts, notes, and other information necessary for data analysis and processing tasks.
Cluster management metadata is indeed managed through the cloud, but it's primarily handled by Databricks rather than stored directly in the customer's account. The Databricks web application itself is not stored within the customer's cloud account; rather, it's a service provided by Databricks.
Reference:
Databricks documentation: Data in Databricks
NEW QUESTION # 73
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?
- A. DELTA
- B. org.apache.spark.sql.sqlite
- C. autoloader
- D. sqlite
- E. org.apache.spark.sql.jdbc
Answer: E
Explanation:
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
NEW QUESTION # 74
A data engineer has realized that they made a mistake when making a daily update to a table. They need to use Delta time travel to restore the table to a version that is 3 days old. However, when the data engineer attempts to time travel to the older version, they are unable to restore the data because the data files have been deleted.
Which of the following explains why the data files are no longer present?
- A. The HISTORY command was run on the table
- B. The TIME TRAVEL command was run on the table
- C. The OPTIMIZE command was nun on the table
- D. The VACUUM command was run on the table
- E. The DELETE HISTORY command was run on the table
Answer: D
NEW QUESTION # 75
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