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Databricks Certified Professional Data Engineer Exam Sample Questions (Q44-Q49):

NEW QUESTION # 44
The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalidlatitudeandlongitudevalues in theactivity_detailstable have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to addCHECKconstraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

A. The activity details table already exists; CHECK constraints can only be added during initial table creation.
B. The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.
C. The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.
D. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.
E. The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.

Answer: E

Explanation:
The failure is that the code to add CHECK constraints to the Delta Lake table fails when executed. The code uses ALTER TABLE ADD CONSTRAINT commands to add two CHECK constraints to a table named activity_details. The first constraint checks if the latitude value is between -90 and 90, and the second constraint checks if the longitude value is between -180 and 180. The cause of this failure is that the activity_details table already contains records that violate these constraints, meaning that they have invalid latitude or longitude values outside of these ranges. When adding CHECK constraints to an existing table, Delta Lake verifies that all existing data satisfies the constraints before adding them to the table. If any record violates the constraints, Delta Lake throws an exception and aborts the operation. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Add a CHECK constraint to an existing table" section.
https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-alter-table.html#add-constraint

NEW QUESTION # 45
A dataset has been defined using Delta Live Tables and includes an expectations clause: CON-STRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL What is the expected behavior when a batch of data containing data that violates these constraints is processed?

A. Records that violate the expectation are added to the target dataset and flagged as in-valid in a field added to the target dataset.
B. Records that violate the expectation cause the job to fail
C. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log.
D. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log.
E. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table.

Answer: B

Explanation:
Explanation
The answer is Records that violate the expectation cause the job to fail.
Delta live tables support three types of expectations to fix bad data in DLT pipelines Review below example code to examine these expectations, Diagram Description automatically generated with medium confidence

Invalid records:
Use the expect operator when you want to keep records that violate the expectation. Records that violate the expectation are added to the target dataset along with valid records:
SQL
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01')
Drop invalid records:
Use the expect or drop operator to prevent the processing of invalid records. Records that violate the expectation are dropped from the target dataset:
SQL
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION DROP ROW Fail on invalid records:
When invalid records are unacceptable, use the expect or fail operator to halt execution immediately when a record fails validation. If the operation is a table update, the system atomically rolls back the transaction:
SQL
CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01') ON VIOLATION FAIL UP-DATE

NEW QUESTION # 46
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
B. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
C. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
D. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
E. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.

Answer: A

Explanation:
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.

NEW QUESTION # 47
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE.
Three datasets are defined against Delta Lake table sources using LIVE TABLE . The table is configured to
run in Development mode using the Triggered Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after
clicking Start to update the pipeline?

A. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to
allow for additional testing
B. All datasets will be updated continuously and the pipeline will not shut down. The compute resources
will persist with the pipeline
C. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will
persist after the pipeline is stopped to allow for additional testing
D. All datasets will be updated once and the pipeline will shut down. The compute resources will be
terminated
E. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will
be deployed for the update and terminated when the pipeline is stopped

Answer: A

NEW QUESTION # 48
A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays:
Which limitation will the team face while diagnosing this problem?

A. New fields not be computed for historic records.
B. Spark cannot capture the topic partition fields from the kafka source.
C. Updating the table schema requires a default value provided for each file added.
D. Updating the table schema will invalidate the Delta transaction log metadata.

Answer: A

Explanation:
When adding new fields to a Delta table's schema, these fields will not be retrospectively applied to historical records that were ingested before the schema change. Consequently, while the team can use the new metadata fields to investigate transient processing delays moving forward, they will be unable to apply this diagnostic approach to past data that lacks these fields.
References:
* Databricks documentation on Delta Lake schema management: https://docs.databricks.com/delta/delta- batch.html#schema-management

NEW QUESTION # 49
......

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