Databricks Certified Data Engineer Associate170 questions across 17 pages are complete and free, with no account or card.
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Which of the following tools is used by Auto Loader process data incrementally?
Answer: B
Databricks uses Apache Spark Structured Streaming to back numerous product associated with ingestion workloads, including: - Auto Loader - COPY INTO - Delta Live Tables pipelines - Materialized views and streaming tables in Databricks SQL Source: https://docs.databricks.com/en/ingestion/streaming.html
Source: Adapted from community discussion — res3
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?
Answer: D
The correct line of code to fill in the blank to execute a micro-batch to process data every 5 seconds is: D. trigger(processingTime="5 seconds") Option A ("trigger("5 seconds")") would not work because it does not specify that the trigger should be a processing time trigger, which is necessary to trigger a micro-batch processing at regular intervals. Option B ("trigger()") would not work because it would use the default trigger, which is not a processing time trigger. Option C ("trigger(once="5 seconds")") would not work because it would only trigger the query once, not at regular intervals. Option E ("trigger(continuous="5 seconds")") would not work because it would trigger the query to run continuously, without any pauses in between, which is not what the data engineer wants.
Source: Adapted from community discussion — 4be8126
Which of the following describes when to use the CREATE STREAMING LIVE TABLE (formerly CREATE INCREMENTAL LIVE TABLE) syntax over the CREATE LIVE TABLE syntax when creating Delta Live Tables (DLT) tables using SQL?
Answer: B
B. CREATE STREAMING LIVE TABLE should be used when data needs to be processed incrementally. The CREATE STREAMING LIVE TABLE syntax is used to create tables that read data incrementally, while the CREATE LIVE TABLE syntax is used to create tables that read data in batch mode. Delta Live Tables support both streaming and batch modes of processing data. When the data is streamed and needs to be processed incrementally, CREATE STREAMING LIVE TABLE should be used.
Source: Adapted from community discussion — 4be8126
A data engineer has three tables in a Delta Live Tables (DLT) pipeline. They have configured the pipeline to drop invalid records at each table. They notice that some data is being dropped due to quality concerns at some point in the DLT pipeline. They would like to determine at which table in their pipeline the data is being dropped. Which of the following approaches can the data engineer take to identify the table that is dropping the records?
Answer: D
D. They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics. To identify the table in a Delta Live Tables (DLT) pipeline where data is being dropped due to quality concerns, the data engineer can navigate to the DLT pipeline page, click on each table in the pipeline, and view the data quality statistics. These statistics often include information about records dropped, violations of expectations, and other data quality metrics. By examining the data quality statistics for each table in the pipeline, the data engineer can determine at which table the data is being dropped.
Source: Adapted from community discussion — vctrhugo
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?
Answer: B
It seems there is some confusion on what dependency means in this case. Option B is correct because adding the new task as a dependency of the original task means that the new task will run BEFORE the original task, which is the goal defined in the question.
Source: Adapted from community discussion — Redwings538
An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query. For the first week following the project’s release, the manager wants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project’s release. Which of the following approaches can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project’s release?
Answer: E
The query scheduler only gives the option on what the interval is to run the query. It does not provide a way to stop after x iterations or at a point in time. The question is confusing. From what i found the only option is to limit users access to the query (and therefore query scheduler). https://docs.databricks.com/security/auth-authz/access-control/query-acl.html Not convinced how this would be helping the organization save money if no-one is manually stopping the schedule. Answer C seems most correct Answer D can be achieved using acl however how is this helpful in the use case described?
Source: Adapted from community discussion — BigDaddyAus
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?
Answer: B
Answer B is correct For those who's selected the same answer as the question 40 in the Databricks exam training, be careful becaue it's quite different: - Here the question is about simultaneously runs -> Scale Out clusters (involves adding more clusters) - In the Databricks exam training, the question is about "sequentially run queries" -> Scale Up (increasing the size of the nodes) Pleas refer to the this accepted answer https://community.databricks.com/t5/data-engineering/sequential-vs-concurrency-optimization-questions-from-query/td-p/36696
Source: Adapted from community discussion — mokrani
Which of the following benefits of using the Databricks Lakehouse Platform is provided by Delta Lake?
Answer: D
D. The ability to support batch and streaming workloads Delta Lake is a key component of the Databricks Lakehouse Platform that provides several benefits, and one of the most significant benefits is its ability to support both batch and streaming workloads seamlessly. Delta Lake allows you to process and analyze data in real-time (streaming) as well as in batch, making it a versatile choice for various data processing needs. While the other options may be benefits or capabilities of Databricks or the Lakehouse Platform in general, they are not specifically associated with Delta Lake.
Source: Adapted from community discussion — vctrhugo
A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary. 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?
Answer: C
The data engineer can use the Auto Stop feature to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard. The Auto Stop feature allows the SQL endpoint to automatically shut down when there are no active connections, which will minimize the total running time of the SQL endpoint. By scheduling the dashboard to refresh once per day, the SQL endpoint will only be running for a short period of time each day, which will minimize the total running time and reduce costs.
Source: Adapted from community discussion — 4be8126
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?
Answer: C
https://docs.databricks.com/en/lakehouse-monitoring/monitor-alerts.html Monitor alerts are created and used the same way as other Databricks SQL alerts. You create a Databricks SQL query on the monitor profile metrics table or drift metrics table. You then create a Databricks SQL alert for this query. You can configure the alert to evaluate the query at a desired frequency, and send a notification if the alert is triggered. By default, email notification is sent. You can also set up a webhook or send notifications to other applications such as Slack or Pagerduty.
Source: Adapted from community discussion — AndreFR
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