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Databricks Certified Professional Data Engineer Exam Sample Questions (Q11-Q16):
NEW QUESTION # 11
The data engineering team is looking to add a new column to the table, but the QA team would like to test the change before implementing in production, which of the below options allow you to quickly copy the table from Prod to the QA environment, modify and run the tests?
- A. ZERO COPY CLONE
- B. SHALLOW CLONE
- C. SHADOW CLONE
- D. DEEP CLONE
- E. METADATA CLONE
Answer: B
Explanation:
Explanation
The answer is SHALLOW CLONE
SHALLOW CLONE If you wish to create a copy of a table quickly to test out applying changes without the risk of modifying the current table, SHALLOW CLONE can be a good option. Shallow clones just copy the Delta transaction logs, meaning that the data doesn't move so it can be very quick.
1.CREATE OR REPLACE TABLE {new_table_name} SHALLOW CLONE
{source_table_name}|[LOCATION path]
DEEP CLONE fully copies data and metadata from a source table to a target. This copy occurs incre-mentally, so executing this command again can sync changes from the source to the target location. It copies all of the data and transaction logs this can take a long time based on the size of the table.
1.CREATE OR REPLACE TABLE {new_table_name} DEEP CLONE {source_table_name}|[LOCATION path]
NEW QUESTION # 12
You are still noticing slowness in query after performing optimize which helped you to resolve the small files problem, the column(transactionId) you are using to filter the data has high cardinality and auto incrementing number. Which delta optimization can you enable to filter data effectively based on this column?
- A. Perform Optimize with Zorder on transactionId
(Correct) - B. Increase the cluster size and enable delta optimization
- C. transactionId has high cardinality, you cannot enable any optimization.
- D. Increase the driver size and enable delta optimization
- E. Create BLOOM FLTER index on the transactionId
Answer: A
Explanation:
Explanation
The answer is, perform Optimize with Z-order by transactionid
Here is a simple explanation of how Z-order works, once the data is naturally ordered, when a flle is scanned it only brings the data it needs into spark's memory Based on the column min and max it knows which data files needs to be scanned.
Table Description automatically generated
Graphical user interface, diagram, application Description automatically generated
NEW QUESTION # 13
You are asked to setup an AUTO LOADER to process the incoming data, this data arrives in JSON format and get dropped into cloud object storage and you are required to process the data as soon as it arrives in cloud storage, which of the following statements is correct
- A. AUTO LOADER can support file notification method so it can process data as it arrives
- B. AUTO LOADER is native to DELTA lake it cannot support external cloud object storage
- C. AUTO LOADER has to be triggered from an external process when the file arrives in the cloud storage
- D. AUTO LOADER needs to be converted to a Structured stream process
- E. AUTO LOADER can only process continuous data when stored in DELTA lake
Answer: A
Explanation:
Explanation
Auto Loader supports two modes when ingesting new files from cloud object storage Directory listing: Auto Loader identifies new files by listing the input directory, and uses a directory polling approach.
File notification: Auto Loader can automatically set up a notification service and queue service that subscribe to file events from the input directory.
Diagram Description automatically generated
File notification is more efficient and can be used to process the data in real-time as data arrives in cloud object storage.
Choosing between file notification and directory listing modes | Databricks on AWS
NEW QUESTION # 14
You are asked to debug a databricks job that is taking too long to run on Sunday's, what are the steps you are going to take to identify the step that is taking longer to run?
- A. Enable debug mode in the Jobs to see the output activity of a job, output should be available to view.
- B. A notebook activity of job run is only visible when using all-purpose cluster.
- C. Under Workflow UI and jobs select job you want to monitor and select the run, notebook activity can be viewed.
- D. Once a job is launched, you cannot access the job's notebook activity.
- E. Use the compute's spark UI to monitor the job activity.
Answer: C
Explanation:
Explanation
The answer is, Under Workflow UI and jobs select job you want to monitor and select the run, notebook activity can be viewed.
You have the ability to view current active runs or completed runs, once you click the run you can see the A picture containing graphical user interface Description automatically generated
Click on the run to view the notebook output
Graphical user interface, text, application, email Description automatically generated
NEW QUESTION # 15
A data engineering team is in the process of converting their existing data pipeline to utilize Auto Loader for
incremental processing in the ingestion of JSON files. One data engineer comes across the following code
block in the Auto Loader documentation:
1. (streaming_df = spark.readStream.format("cloudFiles")
2. .option("cloudFiles.format", "json")
3. .option("cloudFiles.schemaLocation", schemaLocation)
4. .load(sourcePath))
Assuming that schemaLocation and sourcePath have been set correctly, which of the following changes does
the data engineer need to make to convert this code block to use Auto Loader to ingest the data?
- A. There is no change required. The data engineer needs to ask their administrator to turn on Auto Loader
- B. There is no change required. Databricks automatically uses Auto Loader for streaming reads
- C. The data engineer needs to change the format("cloudFiles") line to format("autoLoader")
- D. The data engineer needs to add the .autoLoader line before the .load(sourcePath) line
- E. There is no change required. The inclusion of format("cloudFiles") enables the use of Auto Loader
Answer: E
NEW QUESTION # 16
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