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Databricks Certified Data Engineer Associate Exam Sample Questions (Q24-Q29):

NEW QUESTION # 24
A data engineer has created a new database using the following command:
CREATE DATABASE IF NOT EXISTS customer360;
In which of the following locations will the customer360 database be located?

A. dbfs:/user/hive/customer360
B. dbfs:/user/hive/database/customer360
C. More information is needed to determine the correct response
D. dbfs:/user/hive/warehouse

Answer: D

Explanation:
dbfs:/user/hive/warehouse Thereby showing "dbfs:/user/hive/warehouse/customer360.db The location of the customer360 database depends on the value of the spark.sql.warehouse.dir configuration property, which specifies the default location for managed databases and tables. If the property is not set, the default value is dbfs:/user/hive/warehouse. Therefore, the customer360 database will be located in dbfs:/user/hive/warehouse/customer360.db. However, if the property is set to a different value, such as dbfs:/user/hive/database, then the customer360 database will be located in dbfs:/user/hive/database/customer360.db. Thus, more information is needed to determine the correct response.
Option A is not correct, as dbfs:/user/hive/database/customer360 is not the default location for managed databases and tables, unless the spark.sql.warehouse.dir property is explicitly set to dbfs:/user/hive/database.
Option B is not correct, as dbfs:/user/hive/warehouse is the default location for the root directory of managed databases and tables, not for a specific database. The database name should be appended with .db to the directory path, such as dbfs:/user/hive/warehouse/customer360.db.
Option C is not correct, as dbfs:/user/hive/customer360 is not a valid location for a managed database, as it does not follow the directory structure specified by the spark.sql.warehouse.dir property.
Reference:
Databases and Tables
[Databricks Data Engineer Professional Exam Guide]

NEW QUESTION # 25
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 without notifications.
B. They can set up an Alert with a new email alert destination.
C. They can set up an Alert with a new webhook alert destination.
D. They can set up an Alert with one-time notifications.
E. They can set up an Alert with a custom template.

Answer: C

Explanation:
Explanation
To achieve this, the data engineer can set up an Alert in the Databricks workspace that triggers when the query results exceed the threshold of 100 NULL values. They can create a new webhook alert destination in the Alert's configuration settings and provide the necessary messaging webhook URL to receive notifications.
When the Alert is triggered, it will send a message to the configured webhook URL, which will then notify the entire team of the issue.

NEW QUESTION # 26
A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team's queries uses the same SQL endpoint.
Which of the following approaches can the data engineering team use to improve the latency of the team's queries?

A. They can increase the maximum bound of the SQL endpoint's scaling range.
B. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to "Reliability Optimized."
C. They can turn on the Serverless feature for the SQL endpoint.
D. They can turn on the Auto Stop feature for the SQL endpoint.
E. They can increase the cluster size of the SQL endpoint.

Answer: D

Explanation:
https://community.databricks.com/t5/data-engineering/sequential-vs-concurrency-optimization-questions-from-query/td-p/36696

NEW QUESTION # 27
Which of the following approaches should be used to send the Databricks Job owner an email in the case that the Job fails?

A. Manually programming in an alert system in each cell of the Notebook
B. MLflow Model Registry Webhooks
C. Setting up an Alert in the Job page
D. Setting up an Alert in the Notebook
E. There is no way to notify the Job owner in the case of Job failure

Answer: C

Explanation:
To send the Databricks Job owner an email in the case that the Job fails, the best approach is to set up an Alert in the Job page. This way, the Job owner can configure the email address and the notification type for the Job failure event. The other options are either not feasible, not reliable, or not relevant for this task. Manually programming an alert system in each cell of the Notebook is tedious and error-prone. Setting up an Alert in the Notebook is not possible, as Alerts are only available for Jobs and Clusters. There is a way to notify the Job owner in the case of Job failure, so option D is incorrect. MLflow Model Registry Webhooks are used for model lifecycle events, not Job events, so option E is not applicable. Reference:
Add email and system notifications for job events
Alerts
MLflow Model Registry Webhooks

NEW QUESTION # 28
A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also used by other processes. As a result, the files should be kept as is and will accumulate in the directory.
The data engineer needs to identify which files are new since the previous run in the pipeline, and set up the pipeline to only ingest those new files with each run.
Which of the following tools can the data engineer use to solve this problem?

A. Data Explorer
B. Auto Loader
C. Unity Catalog
D. Delta Lake
E. Databricks SQL

Answer: B

Explanation:
Auto Loader is a tool that can incrementally and efficiently process new data files as they arrive in cloud storage without any additional setup. Auto Loader provides a Structured Streaming source called cloudFiles, which automatically detects and processes new files in a given input directory path on the cloud file storage.
Auto Loader also tracks the ingestion progress and ensures exactly-once semantics when writing data into Delta Lake. Auto Loader can ingest various file formats, such as JSON, CSV, XML, PARQUET, AVRO, ORC, TEXT, and BINARYFILE. Auto Loader has support for both Python and SQL in Delta Live Tables, which are a declarative way to build production-quality data pipelines with Databricks. References: What is Auto Loader?, Get started with Databricks Auto Loader, Auto Loader in Delta Live Tables

NEW QUESTION # 29
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