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

Section	Weight	Objectives
Data Governance	7%	- Unity Catalog management - Data lineage and metadata tracking - Policy enforcement
Data Transformation, Cleansing, and Quality	10%	- Standardization and normalization - Data validation and quality checks - Handling missing or inconsistent data
Cost & Performance Optimisation	13%	- Storage optimization (partitioning, Z-order, indexing) - Cluster configuration and scaling - Query optimization and caching
Data Sharing and Federation	5%	- Cross-workspace and cross-cloud access - Unity Catalog data sharing
Debugging and Deploying	10%	- Deployment using bundles, CLI, and APIs - Troubleshooting pipelines and errors - CI/CD and DevOps practices
Data Modelling	6%	- Medallion Architecture implementation - Delta Lake table design - Schema design and management
Data Ingestion & Acquisition	7%	- Connecting to diverse data sources - Schema inference and evolution - Auto Loader and streaming ingestion
Ensuring Data Security and Compliance	10%	- Compliance standards implementation - Access control and permissions - Data encryption and masking
Developing Code for Data Processing using Python and SQL	22%	- Data transformation and aggregation - Batch and incremental processing logic - Integration with Databricks APIs and tools
Monitoring and Alerting	10%	- Setting up alerts and notifications - Performance and health monitoring - Pipeline observability and logging

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q137-Q142):

NEW QUESTION # 137
Which statement regarding stream-static joins and static Delta tables is correct?

A. Stream-static joins cannot use static Delta tables because of consistency issues.
B. The checkpoint directory will be used to track updates to the static Delta table.
C. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
D. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
E. The checkpoint directory will be used to track state information for the unique keys present in the join.

Answer: D

Explanation:
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch. Verified Reference: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Stream and static joins" section.

NEW QUESTION # 138
A table customerLocations exists with the following schema:
1. id STRING,
2. date STRING,
3. city STRING,
4. country STRING
A senior data engineer wants to create a new table from this table using the following command:
1. CREATE TABLE customersPerCountry AS
2. SELECT country,
3. COUNT(*) AS customers
4. FROM customerLocations
5. GROUP BY country;
A junior data engineer asks why the schema is not being declared for the new table. Which of the following
responses explains why declaring the schema is not necessary?

A. CREATE TABLE AS SELECT statements assign all columns the type STRING
B. CREATE TABLE AS SELECT statements result in tables that do not support schemas
C. CREATE TABLE AS SELECT statements result in tables where schemas are optional
D. CREATE TABLE AS SELECT statements infer the schema by scanning the data
E. CREATE TABLE AS SELECT statements adopt schema details from the source table and query

Answer: E

NEW QUESTION # 139
A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records.
In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?

A. Perform a full outer join on a unique key and overwrite existing data.
B. Perform an insert-only merge with a matching condition on a unique key.
C. Rely on Delta Lake schema enforcement to prevent duplicate records.
D. VACUUM the Delta table after each batch completes.
E. Set the configuration delta.deduplicate = true.

Answer: B

NEW QUESTION # 140
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

A. Cluster: Existing All-Purpose Cluster;
Retries: None;
Maximum Concurrent Runs: 1
B. Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
C. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
D. Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1
E. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1

Answer: C

Explanation:
The configuration that automatically recovers from query failures and keeps costs low is to use a new job cluster, set retries to unlimited, and set maximum concurrent runs to 1. This configuration has the following advantages:
A new job cluster is a cluster that is created and terminated for each job run. This means that the cluster resources are only used when the job is running, and no idle costs are incurred. This also ensures that the cluster is always in a clean state and has the latest configuration and libraries for the job1.
Setting retries to unlimited means that the job will automatically restart the query in case of any failure, such as network issues, node failures, or transient errors. This improves the reliability and availability of the streaming job, and avoids data loss or inconsistency2.
Setting maximum concurrent runs to 1 means that only one instance of the job can run at a time. This prevents multiple queries from competing for the same resources or writing to the same output location, which can cause performance degradation or data corruption3.
Therefore, this configuration is the best practice for scheduling Structured Streaming jobs for production, as it ensures that the job is resilient, efficient, and consistent.

NEW QUESTION # 141
You are currently asked to work on building a data pipeline, you have noticed that you are currently working with a data source that has a lot of data quality issues and you need to monitor data quality and enforce it as part of the data ingestion process, which of the following tools can be used to address this problem?

A. AUTO LOADER
B. JOBS and TASKS
C. STRUCTURED STREAMING with MULTI HOP
D. UNITY Catalog and Data Governance
E. DELTA LIVE TABLES

Answer: E

Explanation:
Explanation
The answer is, DELTA LIVE TABLES
Delta live tables expectations can be used to identify and quarantine bad data, all of the data quality metrics are stored in the event logs which can be used to later analyze and monitor.
DELTA LIVE Tables expectations
Below are three types of expectations, make sure to pay attention differences between these three.
Retain 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:
Python
1.@dlt.expect("valid timestamp", "col("timestamp") > '2012-01-01'")
SQL
1.CONSTRAINT valid_timestamp EXPECT (timestamp > '2012-01-01')
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:
Python
1.@dlt.expect_or_drop("valid_current_page", "current_page_id IS NOT NULL AND cur-rent_page_title IS NOT NULL") SQL
1.CONSTRAINT valid_current_page EXPECT (current_page_id IS NOT NULL and cur-rent_page_title IS NOT NULL) ON VIOLATION DROP ROW Fail on invalid records:
When invalid records are unacceptable, use the expect or fail operator to halt execution imme-diately when a record fails validation. If the operation is a table update, the system atomically rolls back the transaction:
Python
1.@dlt.expect_or_fail("valid_count", "count > 0")
SQL
1.CONSTRAINT valid_count EXPECT (count > 0) ON VIOLATION FAIL UPDATE

NEW QUESTION # 142
......

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