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Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:

Section	Objectives
Topic 1: Production Pipelines and Orchestration	- Automate ETL pipelines and scheduling - Build and manage workflows using Databricks Jobs - Pipeline reliability and fault tolerance
Topic 2: Data Modeling and Storage	- Design scalable data lakehouse architectures - Schema evolution and data partitioning strategies - Delta Lake table design and optimization
Topic 3: Data Ingestion and Transformation	- Ingest data using Apache Spark and Databricks - Transform and clean datasets using Spark SQL and DataFrame APIs - Handle batch and streaming data pipelines
Topic 4: Security, Governance, Monitoring, and Optimization	- Implement Unity Catalog governance and access control - Monitor and optimize Spark workloads - Cost optimization and performance tuning

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Databricks Certified Professional Data Engineer Exam Databricks-Certified-Professional-Data-Engineer Prüfungsfragen mit Lösungen (Q209-Q214):

209. Frage
Why does AUTO LOADER require schema location?

A. Schema location is used to store user provided schema
B. AUTO LOADER does not require schema location, because its supports Schema evolution
C. Schema location is used to identify the schema of target table and source table
D. Schema location is used to store schema inferred by AUTO LOADER
E. Schema location is used to identify the schema of target table

Antwort: D

Begründung:
Explanation
The answer is, Schema location is used to store schema inferred by AUTO LOADER, so the next time AUTO LOADER runs faster as does not need to infer the schema every single time by trying to use the last known schema.
Auto Loader samples the first 50 GB or 1000 files that it discovers, whichever limit is crossed first. To avoid incurring this inference cost at every stream start up, and to be able to provide a stable schema across stream restarts, you must set the option cloudFiles.schemaLocation. Auto Loader creates a hidden directory _schemas at this location to track schema changes to the input data over time.
The below link contains detailed documentation on different options
Auto Loader options | Databricks on AWS

210. Frage
A Delta Lake table with Change Data Feed (CDF) enabled in the Lakehouse named customer_churn_params is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources. The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours. Which approach would simplify the identification of these changed records?

A. Replace the current overwrite logic with a MERGE statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the Change Data Feed.
B. Modify the overwrite logic to include a field populated by calling current_timestamp() as data are being written; use this field to identify records written on a particular date.
C. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.
D. Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.

Antwort: A

Begründung:
Comprehensive and Detailed Explanation From Exact Extract:
* Exact extract: "Change data feed (CDF) provides row-level change information for Delta tables."
* Exact extract: "Use table_changes to query the set of rows that were inserted, updated, or deleted between two versions (or timestamps)." References: Delta Lake Change Data Feed; Delta Lake MERGE INTO.

211. Frage
Given the following error traceback:
AnalysisException: cannot resolve 'heartrateheartrateheartrate' given input columns:
[spark_catalog.database.table.device_id, spark_catalog.database.table.heartrate, spark_catalog.database.table.mrn, spark_catalog.database.table.time] The code snippet was:
display(df.select(3*"heartrate"))
Which statement describes the error being raised?

A. There is no column in the table named heartrateheartrateheartrate.
B. There is a type error because a DataFrame object cannot be multiplied.
C. There is a syntax error because the heartrate column is not correctly identified as a column.
D. There is a type error because a column object cannot be multiplied.

Antwort: A

Begründung:
Comprehensive and Detailed Explanation From Exact Extract:
* Exact extract: "select() expects column names or Column expressions."
* Exact extract: "When using strings directly, Spark SQL interprets them as literal column names."
* Exact extract: "Python string operations, such as "colname"*3, return repeated strings, not column expressions." The expression 3*"heartrate" is Python string multiplication, which evaluates to "heartrateheartrateheartrate".
The select() method interprets this as a literal column name. Since there is no column with that name in the DataFrame schema, Spark raises AnalysisException saying it cannot resolve that column. To correctly multiply a column by a scalar, one must use the column expression form:
from pyspark.sql.functions import col
df.select((col("heartrate") * 3).alias("heartrate_x3"))
This ensures Spark evaluates the arithmetic operation on the column instead of misinterpreting the string.
References: PySpark DataFrame select; PySpark Column expressions with col().

212. Frage
A data engineer needs to create a database called customer360 at the loca-tion /customer/customer360. The
data engineer is unsure if one of their colleagues has already created the database.
Which of the following commands should the data engineer run to complete this task?

A. CREATE DATABASE customer360 DELTA LOCATION '/customer/customer360';
B. CREATE DATABASE IF NOT EXISTS customer360;
C. CREATE DATABASE IF NOT EXISTS customer360 DELTA LOCATION '/customer/customer360';
D. CREATE DATABASE IF NOT EXISTS customer360 LOCATION '/customer/customer360';
E. CREATE DATABASE customer360 LOCATION '/customer/customer360';

Antwort: D

213. Frage
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 refresh every 1 day from the query's page in Databricks SQL.
B. They can schedule the query to run every 1 day from the Jobs UI
C. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL
D. They can schedule the query to run every 12 hours from the Jobs UI.
E. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL.

Antwort: A

Begründung:
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

214. Frage
......

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