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Databricks Certified-Data-Engineer-Professional Exam Overview:
| Certification Vendor: | Databricks |
| Exam Name: | Databricks Certified Data Engineer Professional |
| Exam Number: | Certified Data Engineer Professional |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 59 scored questions |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Databricks Certified Data Engineer Associate |
| Exam Price: | USD 200, plus applicable taxes as required by local law |
| Available Languages: | English |
| Exam Format: | Multiple-choice |
| Passing Score: | Not publicly specified in the current official exam guide |
| Sample Questions: | Databricks Certified-Data-Engineer-Professional Sample Questions |
| Exam Way: | Online proctored or test center proctored |
| Pre Condition: | No mandatory prerequisite. Databricks recommends related course attendance and approximately one year of hands-on experience performing the Data Engineering tasks covered by the exam. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/data-engineer-professional |
Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Modelling | - Scalable Data Models
|
| Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
| Developing Code for Data Processing using Python and SQL | - Using Python and Tools for Development
|
| Cost & Performance Optimisation | - Delta Optimization
|
| Debugging and Deploying | - Debugging and Troubleshooting
|
| Data Sharing and Federation | - Delta Sharing
|
| Data Governance | - Unity Catalog Permissions
|
| Ensuring Data Security and Compliance | - Data Security
|
| Data Transformation, Cleansing, and Quality | - Advanced Data Transformation
|
| Monitoring and Alerting | - Monitoring
|
Databricks Certified Data Engineer Professional Sample Questions:
1. 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) Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
C) The checkpoint directory will be used to track state information for the unique keys present in the join.
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 updates to the static Delta table.
2. A workspace admin has created a new catalog called finance_data and wants to delegate permission management to a finance team lead without giving them full admin rights. Which privilege should be granted to the finance team lead?
A) GRANT OPTION privilege on the finance_data catalog.
B) ALL PRIVILEGES on the finance_data catalog.
C) MANAGE privilege on the finance_data catalog.
D) Make the finance team lead a metastore admin.
3. An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?
A) input_dict = input()
date= input_dict["date"]
B) import sys
date = sys.argv[1]
C) date = spark.conf.get("date")
D) dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")
E) date = dbutils.notebooks.getParam("date")
4. When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?
A) Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
B) Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
C) Cluster: New Job 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: None;
Maximum Concurrent Runs: 1
5. A nightly batch job is configured to ingest all data files from a cloud object storage container where records are stored in a nested directory structure YYYY/MM/DD. The data for each date represents all records that were processed by the source system on that date, noting that some records may be delayed as they await moderator approval. Each entry represents a user review of a product and has the following schema:
user_id STRING, review_id BIGINT, product_id BIGINT, review_timestamp TIMESTAMP, review_text STRING The ingestion job is configured to append all data for the previous date to a target table reviews_raw with an identical schema to the source system. The next step in the pipeline is a batch write to propagate all new records inserted into reviews_raw to a table where data is fully deduplicated, validated, and enriched.
Which solution minimizes the compute costs to propagate this batch of data?
A) Use Delta Lake version history to get the difference between the latest version of reviews_raw and one version prior, then write these records to the next table.
B) Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
C) Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.
D) Reprocess all records in reviews_raw and overwrite the next table in the pipeline.
E) Perform a batch read on the reviews_raw table and perform an insert-only merge using the natural composite key user_id, review_id, product_id, review_timestamp.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: B |
Databricks Certified-Data-Engineer-Professional Exam Overview:
| Certification Vendor: | Databricks |
| Exam Name: | Databricks Certified Data Engineer Professional |
| Exam Number: | Certified Data Engineer Professional |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 59 scored questions |
| Certificate Validity Period: | 2 years |
| Related Certifications: | Databricks Certified Data Engineer Associate |
| Exam Price: | USD 200, plus applicable taxes as required by local law |
| Available Languages: | English |
| Exam Format: | Multiple-choice |
| Passing Score: | Not publicly specified in the current official exam guide |
| Sample Questions: | Databricks Certified-Data-Engineer-Professional Sample Questions |
| Exam Way: | Online proctored or test center proctored |
| Pre Condition: | No mandatory prerequisite. Databricks recommends related course attendance and approximately one year of hands-on experience performing the Data Engineering tasks covered by the exam. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/data-engineer-professional |
Databricks Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Data Modelling | - Scalable Data Models
|
| Data Ingestion & Acquisition | - Design and implement data ingestion pipelines
|
| Developing Code for Data Processing using Python and SQL | - Using Python and Tools for Development
|
| Cost & Performance Optimisation | - Delta Optimization
|
| Debugging and Deploying | - Debugging and Troubleshooting
|
| Data Sharing and Federation | - Delta Sharing
|
| Data Governance | - Unity Catalog Permissions
|
| Ensuring Data Security and Compliance | - Data Security
|
| Data Transformation, Cleansing, and Quality | - Advanced Data Transformation
|
| Monitoring and Alerting | - Monitoring
|
Databricks Certified Data Engineer Professional Sample Questions:
1. 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) Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
C) The checkpoint directory will be used to track state information for the unique keys present in the join.
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 updates to the static Delta table.
2. A workspace admin has created a new catalog called finance_data and wants to delegate permission management to a finance team lead without giving them full admin rights. Which privilege should be granted to the finance team lead?
A) GRANT OPTION privilege on the finance_data catalog.
B) ALL PRIVILEGES on the finance_data catalog.
C) MANAGE privilege on the finance_data catalog.
D) Make the finance team lead a metastore admin.
3. An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?
A) input_dict = input()
date= input_dict["date"]
B) import sys
date = sys.argv[1]
C) date = spark.conf.get("date")
D) dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")
E) date = dbutils.notebooks.getParam("date")
4. When scheduling Structured Streaming jobs for production, which configuration automatically recovers from query failures and keeps costs low?
A) Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
B) Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
C) Cluster: New Job 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: None;
Maximum Concurrent Runs: 1
5. A nightly batch job is configured to ingest all data files from a cloud object storage container where records are stored in a nested directory structure YYYY/MM/DD. The data for each date represents all records that were processed by the source system on that date, noting that some records may be delayed as they await moderator approval. Each entry represents a user review of a product and has the following schema:
user_id STRING, review_id BIGINT, product_id BIGINT, review_timestamp TIMESTAMP, review_text STRING The ingestion job is configured to append all data for the previous date to a target table reviews_raw with an identical schema to the source system. The next step in the pipeline is a batch write to propagate all new records inserted into reviews_raw to a table where data is fully deduplicated, validated, and enriched.
Which solution minimizes the compute costs to propagate this batch of data?
A) Use Delta Lake version history to get the difference between the latest version of reviews_raw and one version prior, then write these records to the next table.
B) Configure a Structured Streaming read against the reviews_raw table using the trigger once execution mode to process new records as a batch job.
C) Filter all records in the reviews_raw table based on the review_timestamp; batch append those records produced in the last 48 hours.
D) Reprocess all records in reviews_raw and overwrite the next table in the pipeline.
E) Perform a batch read on the reviews_raw table and perform an insert-only merge using the natural composite key user_id, review_id, product_id, review_timestamp.
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: C | Question # 3 Answer: D | Question # 4 Answer: D | Question # 5 Answer: B |
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