Jun-2024 New Version Databricks-Certified-Professional-Data-Engineer Certificate & Helpful Exam Dumps is Online
Databricks-Certified-Professional-Data-Engineer Free Certification Exam Material with 125 Q&As
Databricks Databricks-Certified-Professional-Data-Engineer is a certification that validates the skills and knowledge of professionals who work with big data and data engineering. Databricks-Certified-Professional-Data-Engineer exam is designed for individuals who have experience in designing, building, and maintaining data pipelines on the Databricks platform. Databricks Certified Professional Data Engineer Exam certification confirms that the candidate has proficiency in building robust, scalable, and efficient big data solutions in the cloud.
Databricks Certified Professional Data Engineer exam is a hands-on exam that requires the candidate to complete a set of tasks using Databricks. Databricks-Certified-Professional-Data-Engineer exam evaluates the candidate's ability to design and implement data pipelines, work with data sources and sinks, and perform transformations using Databricks. Databricks-Certified-Professional-Data-Engineer exam also tests the candidate's ability to optimize and tune data pipelines for performance and reliability.
NEW QUESTION # 41
A SQL Dashboard was built for the supply chain team to monitor the inventory and product orders, but all of the timestamps displayed on the dashboards are showing in UTC format, so they requested to change the time zone to the location of New York. How would you approach resolving this issue?
- A. Change the spark configuration of SQL endpoint to format the timestamp to Ameri-ca/New_York
- B. Under SQL Admin Console, set the SQL configuration parameter time zone to Ameri-ca/New_York
- C. Change the timestamp on the delta tables to America/New_York format
- D. Move the workspace from Central US zone to East US Zone
- E. Add SET Timezone = America/New_York on every of the SQL queries in the dashboard.
Answer: B
Explanation:
Explanation
The answer is, Under SQL Admin Console, set the SQL configuration parameter time zone to America/New_York Here are steps you can take this to configure, so the entire dashboard is changed without changing individual queries Configure SQL parameters To configure all warehouses with SQL parameters:
1.Click Settings at the bottom of the sidebar and select SQL Admin Console.
2.Click the SQL Warehouse Settings tab.
3.In the SQL Configuration Parameters textbox, specify one key-value pair per line. Sepa-rate the name of the parameter from its value using a space. For example, to ena-ble ANSI_MODE:
Graphical user interface, text, application Description automatically generated
Similarly, we can add a line in the SQL Configuration parameters
timezone America/New_York
SQL configuration parameters | Databricks on AWS
NEW QUESTION # 42
The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?
- A. Configure a job that executes every time new data lands in a given directory.
- B. Schedule a jo to execute the pipeline once and hour on a dedicated interactive cluster.
- C. Schedule a job to execute the pipeline once hour on a new job cluster.
- D. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
Answer: C
Explanation:
Scheduling a job to execute the data processing pipeline once an hour on a new job cluster is the most cost-effective solution given the scenario. Job clusters are ephemeral in nature; they are spun up just before the job execution and terminated upon completion, which means you only incur costs for the time the cluster is active. Since the total processing time is only 10 minutes, a new job cluster created for each hourly execution minimizes the running time and thus the cost, while also fulfilling the requirement for hourly data updates for the business reporting team's dashboards.
References:
* Databricks documentation on jobs and job clusters: https://docs.databricks.com/jobs.html
NEW QUESTION # 43
Which of the following describes how Databricks Repos can help facilitate CI/CD workflows on the
Databricks Lakehouse Platform?
- A. Databricks Repos can store the single-source-of-truth Git repository
- B. Databricks Repos can be used to design, develop, and trigger Git automation pipelines
- C. Databricks Repos can facilitate the pull request, review, and approval process before merging branches
- D. Databricks Repos can commit or push code changes to trigger a CI/CD process
- E. Databricks Repos can merge changes from a secondary Git branch into a main Git branch
Answer: D
NEW QUESTION # 44
A data engineer needs to capture pipeline settings from an existing in the workspace, and use them to create and version a JSON file to create a new pipeline.
Which command should the data engineer enter in a web terminal configured with the Databricks CLI?
- A. Stop the existing pipeline; use the returned settings in a reset command
- B. Use the alone command to create a copy of an existing pipeline; use the get JSON command to get the pipeline definition; save this to git
- C. Use the get command to capture the settings for the existing pipeline; remove the pipeline_id and rename the pipeline; use this in a create command
- D. Use list pipelines to get the specs for all pipelines; get the pipeline spec from the return results parse and use this to create a pipeline
Answer: C
Explanation:
The Databricks CLI provides a way to automate interactions with Databricks services. When dealing with pipelines, you can use thedatabricks pipelines get --pipeline-idcommand to capture the settings of an existing pipeline in JSON format. This JSON can then be modified by removing thepipeline_idto prevent conflicts and renaming the pipeline to create a new pipeline. The modified JSON file can then be used with the databricks pipelines createcommand to create a new pipeline with those settings.
References:
* Databricks Documentation on CLI for Pipelines: Databricks CLI - Pipelines
NEW QUESTION # 45
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. TRANSFORM
- C. ARRAYEXPR
- D. APPLY
- E. FORALL
Answer: B
Explanation:
Explanation
TRANSFORM -> Transforms elements in an array in expr using the function func.
1.transform(expr, func)
NEW QUESTION # 46
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 models new data arriving in a data stream as new rows appended to an unbounded table.
- C. Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
- D. Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
- E. Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
Answer: D
Explanation:
This is the correct answer because it characterizes the general programming model used by Spark Structured Streaming, which is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model, where users can express their streaming computation using the same Dataset/DataFrame API as they would use for static data. The Spark SQL engine will take care of running the streaming query incrementally and continuously and updating the final result as streaming data continues to arrive. Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Overview" section.
NEW QUESTION # 47
A Delta table of weather records is partitioned by date and has the below schema:
date DATE, device_id INT, temp FLOAT, latitude FLOAT, longitude FLOAT
To find all the records from within the Arctic Circle, you execute a query with the below filter:
latitude > 66.3
Which statement describes how the Delta engine identifies which files to load?
- A. The Hive metastore is scanned for min and max statistics for the latitude column
- B. All records are cached to attached storage and then the filter is applied
- C. The Delta log is scanned for min and max statistics for the latitude column
- D. The Parquet file footers are scanned for min and max statistics for the latitude column
- E. All records are cached to an operational database and then the filter is applied
Answer: C
Explanation:
This is the correct answer because Delta Lake uses a transaction log to store metadata about each table, including min and max statistics for each column in each data file. The Delta engine can use this information to quickly identify which files to load based on a filter condition, without scanning the entire table or the file footers. This is called data skipping and it can improve query performance significantly. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; [Databricks Documentation], under "Optimizations - Data Skipping" section.
In the Transaction log, Delta Lake captures statistics for each data file of the table. These statistics indicate per file:
- Total number of records
- Minimum value in each column of the first 32 columns of the table
- Maximum value in each column of the first 32 columns of the table
- Null value counts for in each column of the first 32 columns of the table When a query with a selective filter is executed against the table, the query optimizer uses these statistics to generate the query result. it leverages them to identify data files that may contain records matching the conditional filter.
For the SELECT query in the question, The transaction log is scanned for min and max statistics for the price column
NEW QUESTION # 48
The following code has been migrated to a Databricks notebook from a legacy workload:
The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?
- A. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
- B. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.
- C. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
- D. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
- E. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
Answer: D
Explanation:
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.
NEW QUESTION # 49
Which statement describes integration testing?
- A. Validates behavior of individual elements of your application
- B. Validates interactions between subsystems of your application
- C. Requires an automated testing framework
- D. Requires manual intervention
- E. Validates an application use case
Answer: E
Explanation:
This is the correct answer because it describes integration testing. Integration testing is a type of testing that validates interactions between subsystems of your application, such as modules, components, or services.
Integration testing ensures that the subsystems work together as expected and produce the correct outputs or results. Integration testing can be done at different levels of granularity, such as component integration testing, system integration testing, or end-to-end testing. Integration testing can help detect errors or bugs that may not be found by unit testing, which only validates behavior of individual elements of your application. Verified References: [Databricks Certified Data Engineer Professional], under "Testing" section; Databricks Documentation, under "Integration testing" section.
NEW QUESTION # 50
Which of the following commands results in the successful creation of a view on top of the delta stream(stream on delta table)?
- A. Spark.read.format("delta").table("sales").mode("stream").createOrReplaceTempView("streaming_vw")
- B. You can not create a view on streaming data source.
- C. Spark.read.format("delta").table("sales").createOrReplaceTempView("streaming_vw")
- D. Spark.read.format("delta").table("sales").trigger("stream").createOrReplaceTempView("streaming_vw")
- E. Spark.read.format("delta").stream("sales").createOrReplaceTempView("streaming_vw")
- F. Spark.readStream.format("delta").table("sales").createOrReplaceTempView("streaming_vw")
Answer: F
Explanation:
Explanation
The answer is
Spark.readStream.table("sales").createOrReplaceTempView("streaming_vw") When you load a Delta table as a stream source and use it in a streaming query, the query processes all of the data present in the table as well as any new data that arrives after the stream is started.
You can load both paths and tables as a stream, you also have the ability to ignore deletes and changes(updates, Merge, overwrites) on the delta table.
Here is more information,
https://docs.databricks.com/delta/delta-streaming.html#delta-table-as-a-source
NEW QUESTION # 51
Data engineering team is required to share the data with Data science team and both the teams are using different workspaces in the same organizationwhich of the following techniques can be used to simplify sharing data across?
*Please note the question is asking how data is shared within an organization across multiple workspaces.
- A. DELTA lake
- B. DELTA LIVE Pipelines
- C. Use a single storage location
- D. Unity Catalog
- E. Data Sharing
Answer: D
Explanation:
Explanation
The answer is the Unity catalog.
Diagram Description automatically generated
Unity Catalog works at the Account level, it has the ability to create a meta store and attach that meta store to many workspaces see the below diagram to understand how Unity Catalog Works, as you can see a metastore can now be shared with both workspaces using Unity Catalog, prior to Unity Catalog the options was to use single cloud object storage manually mount in the second databricks workspace, and you can see here Unity Catalog really simplifies that.
Diagram Description automatically generated with medium confidence
sorry for the inconvenience watermark was added because other people on Udemy are copying my questions and images.
duct features
https://databricks.com/product/unity-catalog
NEW QUESTION # 52
The data science team has requested assistance in accelerating queries on free form text from user reviews.
The data is currently stored in Parquet with the below schema:
item_id INT, user_id INT, review_id INT, rating FLOAT, review STRING
The review column contains the full text of the review left by the user. Specifically, the data science team is looking to identify if any of 30 key words exist in this field.
A junior data engineer suggests converting this data to Delta Lake will improve query performance.
Which response to the junior data engineer s suggestion is correct?
- A. Text data cannot be stored with Delta Lake.
- B. Delta Lake statistics are only collected on the first 4 columns in a table.
- C. The Delta log creates a term matrix for free text fields to support selective filtering.
- D. ZORDER ON review will need to be run to see performance gains.
- E. Delta Lake statistics are not optimized for free text fields with high cardinality.
Answer: E
Explanation:
Converting the data to Delta Lake may not improve query performance on free text fields with high cardinality, such as the review column. This is because Delta Lake collects statistics on the minimum and maximum values of each column, which are not very useful for filtering or skipping data on free text fields.
Moreover, Delta Lake collectsstatistics on the first 32 columns by default, which may not include the review column if the table has more columns. Therefore, the junior data engineer's suggestion is not correct. A better approach would be to use a full-text search engine, such as Elasticsearch, to index and query the review column. Alternatively, you can use natural language processing techniques, such as tokenization, stemming, and lemmatization, to preprocess the review column and create a new column with normalized terms that can be used for filtering or skipping data. References:
* Optimizations: https://docs.delta.io/latest/optimizations-oss.html
* Full-text search with Elasticsearch: https://docs.databricks.com/data/data-sources/elasticsearch.html
* Natural language processing: https://docs.databricks.com/applications/nlp/index.html
NEW QUESTION # 53
What is the type of table created when you issue SQL DDL command CREATE TABLE sales (id int, units int)
- A. External Table
- B. Managed Parquet table
- C. Managed Delta table
- D. Query fails due to missing location
- E. Query fails due to missing format
Answer: C
Explanation:
Explanation
Answer is Managed Delta table
Anytime a table is created without the Location keyword it is considered a managed table, by de-fault all managed tables DELTA tables Syntax CREATE TABLE table_name ( column column_data_type...)
NEW QUESTION # 54
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Incremental state information should be maintained for 10 minutes for late-arriving data.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:
Choose the response that correctly fills in the blank within the code block to complete this task.
- A. withWatermark("event_time", "10 minutes")
- B. delayWrite("event_time", "10 minutes")
- C. await("event_time + '10 minutes'")
- D. awaitArrival("event_time", "10 minutes")
- E. slidingWindow("event_time", "10 minutes")
Answer: A
Explanation:
The correct answer is A. withWatermark("event_time", "10 minutes"). This is because the question asks for incremental state information to be maintained for 10 minutes for late-arriving data. The withWatermark method is used to define the watermark for late data. The watermark is a timestamp column and a threshold that tells the system how long to wait for late data. In this case, the watermark is set to 10 minutes. The other options are incorrect because they are not valid methods or syntax for watermarking in Structured Streaming. References:
* Watermarking: https://docs.databricks.com/spark/latest/structured-streaming/watermarks.html
* Windowed aggregations:
https://docs.databricks.com/spark/latest/structured-streaming/window-operations.html
NEW QUESTION # 55
The data engineering team has configured a Databricks SQL query and alert to monitor the values in a Delta Lake table. Therecent_sensor_recordingstable contains an identifyingsensor_idalongside thetimestampandtemperaturefor the most recent 5 minutes of recordings.
The below query is used to create the alert:
The query is set to refresh each minute and always completes in less than 10 seconds. The alert is set to trigger whenmean (temperature) > 120. Notifications are triggered to be sent at most every 1 minute.
If this alert raises notifications for 3 consecutive minutes and then stops, which statement must be true?
- A. The source query failed to update properly for three consecutive minutes and then restarted
- B. The maximum temperature recording for at least one sensor exceeded 120 on three consecutive executions of the query
- C. The average temperature recordings for at least one sensor exceeded 120 on three consecutive executions of the query
- D. The total average temperature across all sensors exceeded 120 on three consecutive executions of the query
- E. Therecent_sensor_recordingstable was unresponsive for three consecutive runs of the query
Answer: C
Explanation:
This is the correct answer because the query is using a GROUP BY clause on the sensor_id column, which means it will calculate the mean temperature for each sensor separately. The alert will trigger when the mean temperature for any sensor is greater than 120, which means at least one sensor had an average temperature above 120 for three consecutive minutes. The alert will stop when the mean temperature for all sensors drops below 120. Verified References: [Databricks Certified Data Engineer Professional], under "SQL Analytics" section; Databricks Documentation, under "Alerts" section.
NEW QUESTION # 56
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To become a Databricks Certified Professional Data Engineer, candidates must pass a rigorous exam that covers a wide range of topics related to data engineering. Databricks-Certified-Professional-Data-Engineer exam is designed to test a candidate's ability to design, build, and maintain data pipelines using Databricks tools and technologies. It also tests their understanding of data modeling, data warehousing, and data integration.
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