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Databricks Certified Professional Data Engineer Exam Sample Questions (Q39-Q44):
NEW QUESTION # 39
How does a Delta Lake differ from a traditional data lake?
- A. Delta lake is an open storage format like parquet with additional capabilities that can provide reliability, security, and performance
- B. Delta lake is an open storage format designed to replace flat files with additional capa-bilities that can provide reliability, security, and performance
- C. Delta lake is a caching layer on top of data lake that can provide reliability, security, and performance
- D. Delta lake is Datawarehouse service on top of data lake that can provide reliability, se-curity, and performance
- E. Delta lake is proprietary software designed by Databricks that can provide reliability, security, and performance
Answer: A
Explanation:
Explanation
Answer is, Delta lake is an open storage format like parquet with additional capabilities that can provide reliability, security, and performance Delta lake is
* Open source
* Builds up on standard data format
* Optimized for cloud object storage
* Built for scalable metadata handling
Delta lake is not
* Proprietary technology
* Storage format
* Storage medium
* Database service or data warehouse
NEW QUESTION # 40
Which of the following data workloads will utilize a gold table as its source?
- A. A job that enriches data by parsing its timestamps into a human-readable format
- B. A job that aggregates cleaned data to create standard summary statistics
- C. A job that cleans data by removing malformatted records
- D. A job that queries aggregated data that already feeds into a dashboard
- E. A job that ingests raw data from a streaming source into the Lakehouse
Answer: D
Explanation:
Explanation
The answer is, A job that queries aggregated data that already feeds into a dashboard The gold layer is used to store aggregated data, which are typically used for dashboards and reporting.
Review the below link for more info,
Medallion Architecture – Databricks
Gold Layer:
1. Powers Ml applications, reporting, dashboards, ad hoc analytics
2. Refined views of data, typically with aggregations
3. Reduces strain on production systems
4. Optimizes query performance for business-critical data
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
Purpose of each layer in medallion architecture
NEW QUESTION # 41
The data engineering team is using a bunch of SQL queries to review data quality and monitor the ETL job every day, which of the following approaches can be used to set up a schedule and auto-mate this process?
- A. They can schedule the query to run every 12 hours from the Jobs UI.
- B. They can schedule the query to run every 1 day from the Jobs UI
- C. They can schedule the query to refresh every 1 day from the query’s page in Databricks SQL.
- 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 SQL endpoint’s page in Databricks SQL.
Answer: C
Explanation:
Explanation
Explanation
Individual queries can be refreshed on a schedule basis,
To set the schedule:
1. Click the query info tab.
Graphical user interface, text, application, email Description automatically generated
* Click the link to the right of Refresh Schedule to open a picker with schedule intervals.
Graphical user interface, application Description automatically generated
* Set the schedule.
The picker scrolls and allows you to choose:
* An interval: 1-30 minutes, 1-12 hours, 1 or 30 days, 1 or 2 weeks
* A time. The time selector displays in the picker only when the interval is greater than 1 day and the day selection is greater than 1 week. When you schedule a specific time, Databricks SQL takes input in your computer’s timezone and converts it to UTC. If you want a query to run at a certain time in UTC, you must adjust the picker by your local offset. For example, if you want a query to execute at 00:00 UTC each day, but your current timezone is PDT (UTC-7), you should select 17:00 in the picker:
Graphical user interface Description automatically generated
* Click OK.
Your query will run automatically.
If you experience a scheduled query not executing according to its schedule, you should manually trigger the query to make sure it doesn’t fail. However, you should be aware of the following:
* If you schedule an interval-for example, “every 15 minutes”-the interval is calculated from the last successful execution. If you manually execute a query, the scheduled query will not be executed until the interval has passed.
* If you schedule a time, Databricks SQL waits for the results to be “outdated”. For example, if you have a query set to refresh every Thursday and you manually execute it on Wednesday, by Thursday the results will still be considered “valid”, so the query wouldn’t be scheduled for a new execution. Thus, for example, when setting a weekly schedule, check the last query execution time and expect the scheduled query to be executed on the selected day after that execution is a week old. Make sure not to manually execute the query during this time.
If a query execution fails, Databricks SQL retries with a back-off algorithm. The more failures the further away the next retry will be (and it might be beyond the refresh interval).
Refer documentation for additional info,
https://docs.microsoft.com/en-us/azure/databricks/sql/user/queries/schedule-query
NEW QUESTION # 42
Which of the following techniques structured streaming uses to ensure recovery of failures during stream processing?
- A. Delta time travel
- B. Checkpointing and Watermarking
- C. Write ahead logging and watermarking
- D. Checkpointing and write-ahead logging
- E. The stream will failover to available nodes in the cluster
- F. Checkpointing and Idempotent sinks
Answer: D
Explanation:
Explanation
The answer is Checkpointing and write-ahead logging.
Structured Streaming uses checkpointing and write-ahead logs to record the offset range of data being processed during each trigger interval.
NEW QUESTION # 43
How does Lakehouse replace the dependency on using Data lakes and Data warehouses in a Data and Analytics solution?
- A. Supports ACID transactions.
- B. Supports BI and Machine learning workloads
- C. Support for end-to-end streaming and batch workloads
- D. All the above
- E. Open, direct access to data stored in standard data formats.
Answer: D
Explanation:
Explanation
Lakehouse combines the benefits of a data warehouse and data lakes,
Lakehouse = Data Lake + DataWarehouse
Here are some of the major benefits of a lakehouse
Text, letter Description automatically generated
Lakehouse = Data Lake + DataWarehouse
A picture containing text, blackboard Description automatically generated
NEW QUESTION # 44
……
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