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Databricks-Certified-Data-Engineer-Associate Exam tests the candidates' abilities to perform the necessary tasks needed to become a successful data engineer. Databricks-Certified-Data-Engineer-Associate exam covers a wide range of topics such as data extraction, transformation, and loading (ETL), data modeling, data warehousing, and machine learning. Individuals who have passed Databricks-Certified-Data-Engineer-Associate exam possess the professional knowledge necessary to integrate, manipulate, secure, and monitor data within Databricks.

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Databricks Certified Data Engineer Associate exam is designed for data engineers who work with the Databricks platform and have experience in designing and implementing data pipelines. Databricks-Certified-Data-Engineer-Associate Exam covers a wide range of topics, including data ingestion, transformation, storage, and processing. It also covers advanced concepts such as Spark architecture, performance tuning, and optimization.

Databricks Certified Data Engineer Associate Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
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: A

Explanation:
In Databricks SQL, you can use scheduled query executions to update your dashboards or enable routine alerts. By default, your queries do not have a schedule. To set the schedule, you can use the dropdown pickers to specify the frequency, period, starting time, and time zone. You can also choose to end the schedule on a certain date by selecting the End date checkbox and picking a date from the calendar. This way, you can ensure that the query does not run beyond the first week of the project's release and does not incur any additional cost. Option A is incorrect, as setting a limit to the number of DBUs does not stop the query from running. Option B is incorrect, as there is no option to end the schedule after a certain number of refreshes. Option C is incorrect, as there is a way to ensure the query does not cost the organization money beyond the first week of the project's release. Option D is incorrect, as setting a limit to the number of individuals who can manage the query's refresh schedule does not affect the query's execution or cost. Reference: Schedule a query, Schedule a query - Azure Databricks - Databricks SQL


NEW QUESTION # 36
In order for Structured Streaming to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing, which of the following two approaches is used by Spark to record the offset range of the data being processed in each trigger?

Answer: B

Explanation:
Structured Streaming uses checkpointing and write-ahead logs to record the offset range of the data being processed in each trigger. This ensures that the engine can reliably track the exact progress of the processing and handle any kind of failure by restarting and/or reprocessing. Checkpointing is the mechanism of saving the state of a streaming query to fault-tolerant storage (such as HDFS) so that it can be recovered after a failure. Write-ahead logs are files that record the offset range of the data being processed in each trigger and are written to the checkpoint location before the processing starts. These logs are used to recover the query state and resume processing from the last processed offset range in case of a failure. Reference: Structured Streaming Programming Guide, Fault Tolerance Semantics


NEW QUESTION # 37
A data engineer is processing ingested streaming tables and needs to filter out NULL values in the order_datetime column from the raw streaming table orders_raw and store the results in a new table orders_valid using DLT.
Which code snippet should the data engineer use?

Answer: D


NEW QUESTION # 38
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?

Answer: D

Explanation:
Delta Live Tables Expectations allow defining data quality rules. Records that fail the expectations (such as sensor readings outside valid ranges) can be dropped without stopping the streaming pipeline, ensuring continuous and accurate processing.


NEW QUESTION # 39
In which of the following file formats is data from Delta Lake tables primarily stored?

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. Reference: 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 # 40
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