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Databricks Certified Professional Data Engineer Exam Sample Questions (Q24-Q29):
NEW QUESTION # 24
Which of the following locations in Databricks product architecture hosts jobs/pipelines and queries?
- A. Control plane
- B. JDBC data source
- C. Data plane
- D. Databricks web application
- E. Databricks Filesystem
Answer: A
Explanation:
Explanation
The answer is Control Plane,
Databricks operates most of its services out of a control plane and a data plane, please note serverless features like SQL Endpoint and DLT compute use shared compute in Control pane.
Control Plane: Stored in Databricks Cloud Account
*The control plane includes the backend services that Databricks manages in its own Azure account. Notebook commands and many other workspace configurations are stored in the control plane and encrypted at rest.
Data Plane: Stored in Customer Cloud Account
*The data plane is managed by your Azure account and is where your data resides. This is also where data is processed. You can use Azure Databricks connectors so that your clusters can connect to external data sources outside of your Azure account to ingest data or for storage.
Here is the product architecture diagram highlighted where
NEW QUESTION # 25
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:
Choose the response that correctly fills in the blank within the code block to complete this task.
- A. delayWrite("event_time", "10 minutes")
- B. awaitArrival("event_time", "10 minutes")
- C. slidingWindow("event_time", "10 minutes")
- D. await("event_time + '10 minutes'")
- E. withWatermark("event_time", "10 minutes")
Answer: E
Explanation:
The correct answer is A. withWatermark("event_time", "10 minutes"). This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The other options are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming. References:
* Watermarking: https://docs.databricks.com/spark/latest/structured-streaming/watermarks.html
* Windowed aggregations:
https://docs.databricks.com/spark/latest/structured-streaming/window-operations.html
NEW QUESTION # 26
The data science team has requested assistance in accelerating queries on free form text from user reviews. The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer s suggestion is correct?
- A. Delta Lake statistics are not optimized for free text fields with high cardinality.
- B. Delta Lake statistics are only collected on the first 4 columns in a table.
- C. Text data cannot be stored with Delta Lake.
- D. The Delta log creates a term matrix for free text fields to support selective filtering.
- E. ZORDER ON review will need to be run to see performance gains.
Answer: A
Explanation:
Converting the data to Delta Lake may not improve query performance on free text fields with high cardinality, such as the review column. This is because Delta Lake collects statistics on the minimum and maximum values of each column, which are not very useful for filtering or skipping data on free text fields. Moreover, Delta Lake collects statistics on the first 32 columns by default, which may not include the review column if the table has more columns. Therefore, the junior data engineer's suggestion is not correct. A better approach would be to use a full-text search engine, such as Elasticsearch, to index and query the review column. Alternatively, you can use natural language processing techniques, such as tokenization, stemming, and lemmatization, to preprocess the review column and create a new column with normalized terms that can be used for filtering or skipping data. Reference:
Optimizations: https://docs.delta.io/latest/optimizations-oss.html
Full-text search with Elasticsearch: https://docs.databricks.com/data/data-sources/elasticsearch.html Natural language processing: https://docs.databricks.com/applications/nlp/index.html
NEW QUESTION # 27
Which of the following section in the UI can be used to manage permissions and grants to tables?
- A. User access control lists
- B. Admin UI
- C. Workspace admin settings
- D. Data Explorer
- E. User Settings
Answer: D
Explanation:
Explanation
The answer is Data Explorer
NEW QUESTION # 28
You have configured AUTO LOADER to process incoming IOT data from cloud object storage every 15 mins, recently a change was made to the notebook code to update the processing logic but the team later realized that the notebook was failing for the last 24 hours, what steps team needs to take to reprocess the data that was not loaded after the notebook was corrected?
- A. Autoloader automatically re-processes data that was not loaded
- B. Move the files that were not processed to another location and manually copy the files into the ingestion path to reprocess them
- C. Manually re-load the data
- D. Delete the checkpoint folder and run the autoloader again
- E. Enable back_fill = TRUE to reprocess the data
Answer: A
Explanation:
Explanation
The answer is,
Autoloader automatically re-processes data that was not loaded using the checkpoint.
NEW QUESTION # 29
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