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

Section	Weight	Objectives
Data Processing with Spark	25-30%	- Python and SQL for data engineering 1. Built-in and user-defined functions 2. Performance optimization techniques 3. Spark APIs in Python - Spark DataFrames and Spark SQL 1. Window functions 2. DataFrame operations and transformations 3. Spark SQL queries and functions
Delta Lake	20-25%	- Delta Lake fundamentals 1. Optimize and Z-order 2. ACID transactions 3. Time travel and data versioning - Delta Lake operations 1. Merge, update, delete operations 2. Schema evolution and enforcement 3. Delta Live Tables
Data Ingestion	15-20%	- Streaming ingestion 1. Structured streaming fundamentals 2. Kafka integration - Batch ingestion methods 1. Integration with external systems 2. Spark APIs for ingestion 3. DBR autoloader
Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Bronze, silver, gold data layers 2. Differences between data lake, data warehouse, and lakehouse 3. Data governance fundamentals
Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Jobs and job scheduling 2. Monitoring and alerting 3. Task dependencies and orchestration

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q74-Q79):

NEW QUESTION # 74
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
Which kind of the test does the above line exemplify?

A. Manual
B. functional
C. Unit
D. Integration

Answer: C

Explanation:
A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function.
Testing a mathematical function that calculates the area under a curve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.
References:
* Software Testing Fundamentals: Unit Testing

NEW QUESTION # 75
A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by-cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively.
Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?

A. Production code development should only be done using an IDE; executing code against a local build of open source Spark and Delta Lake will provide the most accurate benchmarks for how code will perform in production.
B. The only way to meaningfully troubleshoot code execution times in development notebooks Is to use production-sized data and production-sized clusters with Run All execution.
C. The Jobs Ul should be leveraged to occasionally run the notebook as a job and track execution time during incremental code development because Photon can only be enabled on clusters launched for scheduled jobs.
D. Calling display () forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results.
E. Scala is the only language that can be accurately tested using interactive notebooks; because the best performance is achieved by using Scala code compiled to JARs. all PySpark and Spark SQL logic should be refactored.

Answer: D

Explanation:
This is the correct answer because it explains which of the following adjustments will get a more accurate measure of how code is likely to perform in production. The adjustment is that calling display() forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results. When developing code in Databricks notebooks, one should be aware of how Spark handles transformations and actions. Transformations are operations that create a new DataFrame or Dataset from an existing one, such as filter, select, or join. Actions are operations that trigger a computation on a DataFrame or Dataset and return a result to the driver program or write it to storage, such as count, show, or save. Calling display() on a DataFrame or Dataset is also an action that triggers a computation and displays the result in a notebook cell. Spark uses lazy evaluation for transformations, which means that they are not executed until an action is called. Spark also uses caching to store intermediate results in memory or disk for faster access in subsequent actions. Therefore, calling display() forces a job to trigger, while many transformations will only add to the logical query plan; because of caching, repeated execution of the same logic does not provide meaningful results. To get a more accurate measure of how code is likely to perform in production, one should avoid calling display() too often or clear the cache before running each cell. Verified Reference: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under "Lazy evaluation" section; Databricks Documentation, under "Caching" section.

NEW QUESTION # 76
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.
The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.
Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

A. Schema inference and evolution on .Databricks ensure that inferred types will always accurately match the data types used by downstream systems.
B. The Tungsten encoding used by Databricks is optimized for storing string data; newly-added native support for querying JSON strings means that string types are always most efficient.
C. Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.
D. Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.
E. Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.

Answer: D

Explanation:
This is the correct answer because it accurately presents information about Delta Lake and Databricks that may impact the decision-making process of a junior data engineer who is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields. Delta Lake and Databricks support schema inference and evolution, which means that they can automatically infer the schema of a table from the source data and allow adding new columns or changing column types without affecting existing queries or pipelines. However, schema inference and evolution may not always be desirable or reliable, especially when dealing with complex or nested data structures or when enforcing data quality and consistency across different systems. Therefore, setting types manually can provide greater assurance of data quality enforcement and avoid potential errors or conflicts due to incompatible or unexpected data types. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Schema inference and partition of streaming DataFrames/Datasets" section.

NEW QUESTION # 77
Where in the Spark UI can one diagnose a performance problem induced by not leveraging predicate push-down?

A. In the Stage's Detail screen, in the Completed Stages table, by noting the size of data read from the Input column
B. In the Executor's log file, by gripping for "predicate push-down"
C. In the Query Detail screen, by interpreting the Physical Plan
D. In the Delta Lake transaction log. by noting the column statistics
E. In the Storage Detail screen, by noting which RDDs are not stored on disk

Answer: C

Explanation:
Explanation
This is the correct answer because it is where in the Spark UI one can diagnose a performance problem induced by not leveraging predicate push-down. Predicate push-down is an optimization technique that allows filtering data at the source before loading it into memory or processing it further. This can improve performance and reduce I/O costs by avoiding reading unnecessary data. To leverage predicate push-down, one should use supported data sources and formats, such as Delta Lake, Parquet, or JDBC, and use filter expressions that can be pushed down to the source. To diagnose a performance problem induced by not leveraging predicate push-down, one can use the Spark UI to access the Query Detail screen, which shows information about a SQL query executed on a Spark cluster. The Query Detail screen includes the Physical Plan, which is the actual plan executed by Spark to perform the query. The Physical Plan shows the physical operators used by Spark, such as Scan, Filter, Project, or Aggregate, and their input and output statistics, such as rows and bytes. By interpreting the Physical Plan, one can see if the filter expressions are pushed down to the source or not, and how much data is read or processed by each operator. Verified References: [Databricks Certified Data Engineer Professional], under "Spark Core" section; Databricks Documentation, under
"Predicate pushdown" section; Databricks Documentation, under "Query detail page" section.

NEW QUESTION # 78
When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?

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

Answer: E

Explanation:
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.
References: Job clusters, Job retries, Maximum concurrent runs

NEW QUESTION # 79
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