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Databricks, a unified analytics platform provider that helps organizations process and analyze large data sets, offers a certification exam for data engineers called the Databricks Certified Professional Data Engineer. Databricks-Certified-Professional-Data-Engineer Exam is designed to test the skills and knowledge of data engineers in building and managing data pipelines, ETL processes, and data architectures within the Databricks platform. Databricks Certified Professional Data Engineer Exam certification is intended to validate the expertise of data engineers in implementing and managing data projects, and to demonstrate their competency in using the Databricks platform.

Databricks Certified Professional Data Engineer Certification Exam is a rigorous and comprehensive certification exam that validates a professional's competence in Databricks data engineering principles and techniques. With the exponential growth of data management and processing, data professionals need certification to show that they are experts in handling data. With Databricks Certified Professional Data Engineer certification, data engineers can confidently build, implement and maintain effective and scalable data engineering solutions.

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q66-Q71):

NEW QUESTION # 66
You are working on IOT data where each device has 5 reading in an array collected in Celsius, you were asked to covert each individual reading from Celsius to Fahrenheit, fill in the blank with an appropriate function that can be used in this scenario.
Schema: deviceId INT, deviceTemp ARRAY<double>

SELECT deviceId, __(deviceTempC,i-> (i * 9/5) + 32) as deviceTempF
FROM sensors

A. MULTIPLY
B. ARRAYEXPR
C. TRANSFORM
D. APPLY
E. FORALL

Answer: C

Explanation:
Explanation
TRANSFORM -> Transforms elements in an array in expr using the function func.
1.transform(expr, func)

NEW QUESTION # 67
Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

A. spark.sql.adaptive.advisoryPartitionSizeInBytes
B. spark.sql.adaptive.coalescePartitions.minPartitionNum
C. spark.sql.autoBroadcastJoinThreshold
D. spark.sql.files.maxPartitionBytes
E. spark.sql.files.openCostInBytes

Answer: D

Explanation:
This is the correct answer because spark.sql.files.maxPartitionBytes is a configuration parameter that directly affects the size of a spark-partition upon ingestion of data into Spark. This parameter configures the maximum number of bytes to pack into a single partition when reading files from file-based sources such as Parquet, JSON and ORC. The default value is 128 MB, which means each partition will be roughly 128 MB in size, unless there are too many small files or only one large file. Verified Reference: [Databricks Certified Data Engineer Professional], under "Spark Configuration" section; Databricks Documentation, under "Available Properties - spark.sql.files.maxPartitionBytes" section.

NEW QUESTION # 68
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: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
C. Cluster: New Job Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: Unlimited
D. Cluster: Existing All-Purpose Cluster;
Retries: Unlimited;
Maximum Concurrent Runs: 1
E. Cluster: New Job Cluster;
Retries: None;
Maximum Concurrent Runs: 1

Answer: B

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 # 69
You currently working with the marketing team to setup a dashboard for ad campaign analysis, since the team is not sure how often the dashboard should be refreshed they have decided to do a manual refresh on an as needed basis. Which of the following steps can be taken to reduce the overall cost of the compute when the team is not using the compute?
*Please note that Databricks recently change the name of SQL Endpoint to SQL Warehouses.

A. They can turn on the Auto Stop feature for the SQL endpoint(SQL Warehouse).
B. They can decrease the maximum bound of the SQL endpoint(SQL Warehouse) scaling range.
C. They can decrease the cluster size of the SQL endpoint(SQL Warehouse).
D. They can turn on the Serverless feature for the SQL endpoint(SQL Warehouse) and change the Spot Instance Policy from "Reliability Optimized" to "Cost optimized"
E. They can turn on the Serverless feature for the SQL endpoint(SQL Warehouse).

Answer: A

Explanation:
Explanation
The answer is, They can turn on the Auto Stop feature for the SQL endpoint(SQL Warehouse).
Use auto stop to automatically terminate the cluster when you are not using it.

NEW QUESTION # 70
Which statement characterizes the general programming model used by Spark Structured Streaming?

A. Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
B. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
C. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
D. Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
E. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.

Answer: D

NEW QUESTION # 71
......

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