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Databricks Certified Data Engineer Associate Exam Sample Questions (Q96-Q101):
NEW QUESTION # 96 
An engineering manager uses a Databricks SQL query to monitor ingestion latency for each data source. The manager checks the results of the query every day, but they are manually rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are updated each day?
- A. They can schedule the query to run every 1 day from the Jobs UI.
 - B. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL.
 - C. They can schedule the query to run every 12 hours from the Jobs UI.
 - D. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL.
 - E. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL.
 
Answer: E
Explanation:
Databricks SQL allows users to schedule queries to run automatically at a specified frequency and time zone. This can help users to keep their dashboards or alerts updated with the latest data. To schedule a query, users need to do the following steps:
In the Query Editor, click Schedule > Add schedule to open a menu with schedule settings.
Choose when to run the query. Use the dropdown pickers to specify the frequency, period, starting time, and time zone. Optionally, select the Show cron syntax checkbox to edit the schedule in Quartz Cron Syntax.
Choose More options to show optional settings. Users can also choose a name for the schedule, and a SQL warehouse to power the query.
Click Create. The query will run automatically according to the schedule.
The other options are incorrect because they do not refer to the correct location or frequency to schedule the query. The query's page in Databricks SQL is the place where users can edit, run, or schedule the query. The SQL endpoint's page in Databricks SQL is the place where users can manage the SQL warehouses and SQL endpoints. The Jobs UI is the place where users can create, run, or schedule jobs that execute notebooks, JARs, or Python scripts. Reference: Schedule a query, What are Databricks SQL alerts?, Jobs.
NEW QUESTION # 97 
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?
- A. Records that violate the expectation cause the job to fail.
 - B. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
 - C. Records that violate the expectation are added to the target dataset and flagged as invalid in a field added to the target dataset.
 - D. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
 - E. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.
 
Answer: B
Explanation:
Delta Live Tables expectations are optional clauses that apply data quality checks on each record passing through a query. An expectation consists of a description, a boolean statement, and an action to take when a record fails the expectation. The ON VIOLATION clause specifies the action to take, which can be one of the following: warn, drop, or fail. The drop action means that invalid records are dropped from the target dataset before data is written to the target. The failure is reported as a metric for the dataset, which can be viewed by querying the Delta Live Tables event log. The event log contains information such as the number of records that violate an expectation, the number of records dropped, and the number of records written to the target dataset. References:
* Manage data quality with Delta Live Tables
* Monitor Delta Live Tables pipelines
* Delta Live Tables SQL language reference
NEW QUESTION # 98 
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?
- A. SELECT * FROM sales
 - B. spark.table("sales")
 - C. There is no way to share data between PySpark and SQL.
 - D. spark.delta.table("sales")
 - E. spark.sql("sales")
 
Answer: B
Explanation:
The data engineering team can use the spark.table method to access the Delta table sales in PySpark. This method returns a DataFrame representation of the Delta table, which can be used for further processing or testing. The spark.table method works for any table that is registered in the Hive metastore or the Spark catalog, regardless of the file format1. Alternatively, the data engineering team can also use the DeltaTable.forPath method to load the Delta table from its path2. Reference: 1: SparkSession | PySpark 3.2.0 documentation 2: Welcome to Delta Lake's Python documentation page - delta-spark 2.4.0 documentation
NEW QUESTION # 99 
In which of the following file formats is data from Delta Lake tables primarily stored?
- A. JSON
 - B. CSV
 - C. A proprietary, optimized format specific to Databricks
 - D. Parquet
 - E. Delta
 
Answer: D
Explanation:
Delta Lake is an open source project that provides ACID transactions, time travel, and other features on top of Apache Parquet, a columnar file format that is widely used for big data analytics. Delta Lake uses versioned Parquet files to store your data in your cloud storage, along with JSON files as transaction logs and checkpoint files to track the changes and ensure data integrity. Delta Lake is compatible with any Apache Hive compatible file format, such as CSV, JSON, or AVRO, but it primarily stores data as Parquet files for better performance and compression. References: How to Create Delta Lake tables, 5 reasons to choose Delta Lake format (on Databricks), Parquet vs Delta format in Azure Data Lake Gen 2 store, What is Delta Lake? - Azure Databricks, Lakehouse and Delta tables - Microsoft Fabric
NEW QUESTION # 100 
A new data engineering team team has been assigned to an ELT project. The new data engineering team will need full privileges on the table sales to fully manage the project.
Which command can be used to grant full permissions on the database to the new data engineering team?
- A. GRANT ALL PRIVILEGES ON TABLE team TO sales;
 - B. grant all privileges on table sales TO team;
 - C. GRANT SELECT CREATE MODIFY ON TABLE sales TO team;
 - D. GRANT SELECT ON TABLE sales TO team;
 
Answer: B
Explanation:
To grant full privileges on a table such as 'sales' to a group like 'team', the correct SQL command in Databricks is:
GRANT ALL PRIVILEGES ON TABLE sales TO team;
This command assigns all available privileges, including SELECT, INSERT, UPDATE, DELETE, and any other data manipulation or definition actions, to the specified team. This is typically necessary when a team needs full control over a table to manage and manipulate it as part of a project or ongoing maintenance.
References:Databricks documentation on SQL permissions: SQL Permissions in Databricks
NEW QUESTION # 101
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