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Databricks Certified Professional Data Engineer exam is a challenging and rigorous exam that requires candidates to have a deep understanding of data engineering concepts and a strong knowledge of the Databricks platform. However, with the right preparation, candidates can pass the exam and achieve this valuable certification. Databricks offers various resources and training programs to help candidates prepare for the exam, including online courses, practice exams, and study guides.
Databricks is a leading company in the field of data engineering and machine learning. The company offers a wide range of services and tools to help organizations manage and analyze their data more effectively. One of the key offerings from Databricks is the Databricks Certified Professional Data Engineer (Databricks-Certified-Professional-Data-Engineer) certification exam. Databricks-Certified-Professional-Data-Engineer Exam is designed to test the skills and knowledge of data engineers who work with Databricks.
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Databricks is a cloud-based data processing and analytics platform that is widely used in the industry. It provides an end-to-end solution for data engineering, machine learning, and business analytics. To validate the skills and expertise of data engineers who work with Databricks, the company offers a certification program known as the Databricks-Certified-Professional-Data-Engineer (Databricks Certified Professional Data Engineer) exam. Databricks Certified Professional Data Engineer Exam certification is designed to help data engineers demonstrate their proficiency in using Databricks to design, build, and maintain data pipelines and data solutions.
NEW QUESTION # 75
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint2.0/jobs/create.
Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?
Answer: D
Explanation:
Explanation
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with a name, an existing cluster id, and a notebook task. However, it does not specify any schedule or trigger for the job execution. Therefore, three new jobs with the same name and configuration will be created in the workspace, but none of them will be executed until they are manually triggered or scheduled.
Verified References: [Databricks Certified Data Engineer Professional], under "Monitoring & Logging" section; [Databricks Documentation], under "Jobs API - Create" section.
NEW QUESTION # 76
Which of the following statements describes Delta Lake?
Answer: B
Explanation:
Explanation
Delta Lake
NEW QUESTION # 77
The data engineering team maintains the following code:
Assuming that this code produces logically correct results and the data in the source tables has been de- duplicated and validated, which statement describes what will occur when this code is executed?
Answer: C
Explanation:
The provided PySpark code performs the following operations:
* Reads Data from silver_customer_sales Table:
* The code starts by accessing the silver_customer_sales table using the spark.table method.
* Groups Data by customer_id:
* The .groupBy("customer_id") function groups the data based on the customer_id column.
* Aggregates Data:
* The .agg() function computes several aggregate metrics for each customer_id:
* F.min("sale_date").alias("first_transaction_date"): Determines the earliest sale date for the customer.
* F.max("sale_date").alias("last_transaction_date"): Determines the latest sale date for the customer.
* F.mean("sale_total").alias("average_sales"): Calculates the average sale amount for the customer.
* F.countDistinct("order_id").alias("total_orders"): Counts the number of unique orders placed by the customer.
* F.sum("sale_total").alias("lifetime_value"): Calculates the total sales amount (lifetime value) for the customer.
* Writes Data to gold_customer_lifetime_sales_summary Table:
* The .write.mode("overwrite").table("gold_customer_lifetime_sales_summary") command writes the aggregated data to the gold_customer_lifetime_sales_summary table.
* The mode("overwrite") specifies that the existing data in the
gold_customer_lifetime_sales_summary table will be completely replaced by the new aggregated data.
Conclusion:
When this code is executed, it reads all records from the silver_customer_sales table, performs the specified aggregations grouped by customer_id, and then overwrites the entire gold_customer_lifetime_sales_summary table with the aggregated results. Therefore, option D accurately describes this process: "The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job." References:
* PySpark DataFrame groupBy
* PySpark Basics
NEW QUESTION # 78
A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on task A.
If tasks A and B complete successfully but task C fails during a scheduled run, which statement describes the resulting state?
Answer: D
Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The resulting state after running the second command is that an external table will be created in the storage container mounted to /mnt/finance_eda_bucket with the new name prod.sales_by_store. The command will not change any data or move any files in the storage container; it will only update the table reference in the metastore and create a new Delta transaction log for the renamed table. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "ALTER TABLE RENAME TO" section; Databricks Documentation, under "Create an external table" section.
NEW QUESTION # 79
How does Lakehouse replace the dependency on using Data lakes and Data warehouses in a Data and Analytics solution?
Answer: B
Explanation:
Explanation
Lakehouse combines the benefits of a data warehouse and data lakes,
Lakehouse = Data Lake + DataWarehouse
Here are some of the major benefits of a lakehouse
Text, letter Description automatically generated
Lakehouse = Data Lake + DataWarehouse
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NEW QUESTION # 80
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