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Databricks Certified Professional Data Engineer is a valuable certification for data professionals looking to demonstrate their expertise in the Databricks platform. Databricks Certified Professional Data Engineer Exam certification validates the skills and knowledge required to design and implement data processing solutions using Databricks and Apache Spark. The DCPDE certification is recognized globally and is highly valued by employers looking for data professionals with expertise in Databricks.
Databricks Certified Professional Data Engineer (DCPDE) is a certification program designed to validate the skills and knowledge of data professionals on the Databricks platform. Databricks Certified Professional Data Engineer Exam certification is aimed at professionals who design, build, and maintain data processing systems using Apache Spark and Databricks. The DCPDE certification demonstrates a comprehensive understanding of the Databricks platform and the ability to design and implement data processing solutions using Spark.
Databricks Certified Professional Data Engineer certification is an excellent choice for individuals who are looking to specialize in data engineering and want to demonstrate their expertise in Databricks technologies. It is also a valuable credential for companies that use Databricks and want to ensure that their employees have the necessary skills to manage and analyze large amounts of data effectively.
NEW QUESTION # 77
Which of the following statements can successfully read the notebook widget and pass the python variable to a SQL statement in a Python notebook cell?
- A. 1.order_date = dbutils.widgets.get("widget_order_date")
2.
3.spark.sql("SELECT * FROM sales WHERE orderDate = order_date") - B. 1.order_date = dbutils.widgets.get("widget_order_date")
2.
3.spark.sql(f"SELECT * FROM sales WHERE orderDate = 'order_date' ") - C. 1.order_date = dbutils.widgets.get("widget_order_date")
2.
3.spark.sql(f"SELECT * FROM sales WHERE orderDate = '${order_date }' ") - D. 1.order_date = dbutils.widgets.get("widget_order_date")
2.
3.spark.sql(f"SELECT * FROM sales WHERE orderDate = '{order_date}' ")
(Correct) - E. 1.order_date = dbutils.widgets.get("widget_order_date")
2.
3.spark.sql(f"SELECT * FROM sales WHERE orderDate = 'f{order_date }'")
Answer: D
NEW QUESTION # 78
Which method is used to solve for coefficients bO, b1, ... bn in your linear regression model:
- A. Ridge and Lasso
- B. Integer programming
- C. Apriori Algorithm
- D. Ordinary Least squares
Answer: D
Explanation:
Explanation : RY = b0 + b1x1+b2x2+ .... +bnxn
In the linear model, the bi's represent the unknown p parameters. The estimates for these unknown parameters
are chosen so that, on average, the model provides a reasonable estimate of a person's income based on age
and education. In other words, the fitted model should minimize the overall error between the linear model and
the actual observations. Ordinary Least Squares (OLS) is a common technique to estimate the parameters
NEW QUESTION # 79
When defining external tables using formats CSV, JSON, TEXT, BINARY any query on the exter-nal tables caches the data and location for performance reasons, so within a given spark session any new files that may have arrived will not be available after the initial query. How can we address this limitation?
- A. CLEAR CACH table_name
- B. REFRESH TABLE table_name
- C. BROADCAST TABLE table_name
- D. UNCACHE TABLE table_name
- E. CACHE TABLE table_name
Answer: B
Explanation:
Explanation
The answer is REFRESH TABLE table_name
REFRESH TABLE table_name will force Spark to refresh the availability of external files and any changes.
When spark queries an external table it caches the files associated with it, so that way if the table is queried again it can use the cached files so it does not have to retrieve them again from cloud object storage, but the drawback here is that if new files are available Spark does not know until the Refresh command is ran.
NEW QUESTION # 80
A DELTA LIVE TABLE pipelines can be scheduled to run in two different modes, what are these two different modes?
- A. Continuous, Incremental
- B. Once, Continuous
- C. Triggered, Continuous
- D. Triggered, Incremental
- E. Once, Incremental
Answer: C
Explanation:
Explanation
The answer is Triggered, Continuous
https://docs.microsoft.com/en-us/azure/databricks/data-engineering/delta-live-tables/delta-live-tables-concepts#-
*Triggered pipelines update each table with whatever data is currently available and then stop the cluster running the pipeline. Delta Live Tables automatically analyzes the dependencies between your tables and starts by computing those that read from external sources. Tables within the pipeline are updated after their dependent data sources have been updated.
*Continuous pipelines update tables continuously as input data changes. Once an update is started, it continues to run until manually stopped. Continuous pipelines require an always-running cluster but ensure that downstream consumers have the most up-to-date data.
NEW QUESTION # 81
Below sample input data contains two columns, one cartId also known as session id, and the second column is called items, every time a customer makes a change to the cart this is stored as an array in the table, the Marketing team asked you to create a unique list of item's that were ever added to the cart by each customer, fill in blanks by choosing the appropriate array function so the query produces below expected result as shown below.
Schema: cartId INT, items Array<INT>
Sample Data
1.SELECT cartId, ___ (___(items)) as items
2.FROM carts GROUP BY cartId
Expected result:
cartId items
1 [1,100,200,300,250]
- A. ARRAY_DISTINCT, ARRAY_UNION
- B. ARRAY_UNION, ARRAY_DISTINT
- C. ARRAY_UNION, FLATTEN
- D. ARRAY_UNION, COLLECT_SET
- E. FLATTEN, COLLECT_UNION
Answer: D
Explanation:
Explanation
COLLECT SET is a kind of aggregate function that combines a column value from all rows into a unique list ARRAY_UNION combines and removes any duplicates, Graphical user interface, application Description automatically generated with medium confidence
NEW QUESTION # 82
While investigating a performance issue, you realized that you have too many small files for a given table, which command are you going to run to fix this issue
- A. MERGE table_name
- B. OPTIMIZE table_name
- C. COMPACT table_name
- D. VACUUM table_name
- E. SHRINK table_name
Answer: B
Explanation:
Explanation
The answer is OPTIMIZE table_name,
Optimize compacts small parquet files into a bigger file, by default the size of the files are determined based on the table size at the time of OPTIMIZE, the file size can also be set manually or adjusted based on the workload.
https://docs.databricks.com/delta/optimizations/file-mgmt.html
Tune file size based on Table size
To minimize the need for manual tuning, Databricks automatically tunes the file size of Delta tables based on the size of the table. Databricks will use smaller file sizes for smaller tables and larger file sizes for larger tables so that the number of files in the table does not grow too large.
Table Description automatically generated
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Top of Form
NEW QUESTION # 83
What is the purpose of a gold layer in Multi-hop architecture?
- A. Preserves grain of original data, without any aggregations
- B. Powers ML applications, reporting, dashboards and adhoc reports.
- C. Optimizes ETL throughput and analytic query performance
- D. Eliminate duplicate records
- E. Data quality checks and schema enforcement
Answer: B
Explanation:
Explanation
The answer is Powers ML applications, reporting, dashboards and adhoc reports.
Review the below link for more info,
Medallion Architecture - Databricks
Gold Layer:
1.Powers Ml applications, reporting, dashboards, ad hoc analytics
2.Refined views of data, typically with aggregations
3.Reduces strain on production systems
4.Optimizes query performance for business-critical data
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
NEW QUESTION # 84
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 decrease the cluster size of the SQL endpoint(SQL Warehouse).
- B. 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"
- C. They can turn on the Serverless feature for the SQL endpoint(SQL Warehouse).
- D. They can decrease the maximum bound of the SQL endpoint(SQL Warehouse) scaling range.
- E. They can turn on the Auto Stop feature for the SQL endpoint(SQL Warehouse).
Answer: E
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 # 85
A junior data engineer needs to create a Spark SQL table my_table for which Spark manages both the data and
the metadata. The metadata and data should also be stored in the Databricks Filesystem (DBFS).
Which of the following commands should a senior data engineer share with the junior data engineer to
complete this task?
- A. 1. CREATE TABLE my_table (id STRING, value STRING) USING DBFS;
- B. 1. CREATE MANAGED TABLE my_table (id STRING, value STRING);
- C. 1. CREATE MANAGED TABLE my_table (id STRING, value STRING) USING
2. org.apache.spark.sql.parquet OPTIONS (PATH "storage-path"); - D. 1. CREATE TABLE my_table (id STRING, value STRING) USING
2. org.apache.spark.sql.parquet OPTIONS (PATH "storage-path") - E. 1. CREATE TABLE my_table (id STRING, value STRING);
Answer: E
NEW QUESTION # 86
you are currently working on creating a spark stream process to read and write in for a one-time micro batch, and also rewrite the existing target table, fill in the blanks to complete the below command sucesfully.
1.spark.table("source_table")
2..writeStream
3..option("____", "dbfs:/location/silver")
4..outputMode("____")
5..trigger(Once=____)
6..table("target_table")
- A. checkpointlocation, overwrite, True
- B. targetlocation, overwrite, True
- C. checkpointlocation, True, complete
- D. checkpointlocation, True, overwrite
- E. checkpointlocation, complete, True
Answer: E
NEW QUESTION # 87
A data engineer has ingested a JSON file into a table raw_table with the following schema:
1.transaction_id STRING,
2.payload ARRAY<customer_id:STRING, date:TIMESTAMP, store_id:STRING>
The data engineer wants to efficiently extract the date of each transaction into a table with the fol-lowing
schema:
1.transaction_id STRING,
2.date TIMESTAMP
Which of the following commands should the data engineer run to complete this task?
- A. 1.SELECT transaction_id, date from payload
2.FROM raw_table; - B. 1.SELECT transaction_id, date
2.FROM raw_table; - C. 1.SELECT transaction_id, explode(payload)
2.FROM raw_table; - D. 1.SELECT transaction_id, payload[date]
2.FROM raw_table; - E. 1.SELECT transaction_id, payload.date
2.FROM raw_table;
Answer: E
NEW QUESTION # 88
Direct query on external files limited options, create external tables for CSV files with header and pipe delimited CSV files, fill in the blanks to complete the create table statement CREATE TABLE sales (id int, unitsSold int, price FLOAT, items STRING)
________
________
LOCATION "dbfs:/mnt/sales/*.csv"
- A. FORMAT CSV
TYPE ( header ="true", delimiter = "|") - B. USING CSV
OPTIONS ( header ="true", delimiter = "|")
(Correct) - C. FORMAT CSV
FORMAT TYPE ( header ="true", delimiter = "|") - D. USING CSV
TYPE ( "true","|") - E. FORMAT CSV
OPTIONS ( "true","|")
Answer: B
Explanation:
Explanation
Answer is
USING CSV
OPTIONS ( header ="true", delimiter = "|")
Here is the syntax to create an external table with additional options
CREATE TABLE table_name (col_name1 col_typ1,..)
USING data_source
OPTIONS (key='value', key2=vla2)
LOCATION = "/location"
NEW QUESTION # 89
Which of the following commands can be used to run one notebook from another notebook?
- A. only job clusters can run notebook
- B. notebook.utils.run("full notebook path")
- C. execute.utils.run("full notebook path")
- D. spark.notebook.run("full notebook path")
- E. dbutils.notebook.run("full notebook path")
Answer: E
Explanation:
Explanation
The answer is dbutils.notebook.run(" full notebook path ")
Here is the full command with additional options.
run(path: String, timeout_seconds: int, arguments: Map): String
1.dbutils.notebook.run("ful-notebook-name", 60, {"argument": "data", "argument2": "data2", ...})
NEW QUESTION # 90
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. APPLY
- D. ARRAYEXPR
- E. FORALL
Answer: B
Explanation:
Explanation
TRANSFORM -> Transforms elements in an array in expr using the function func.
1.transform(expr, func)
NEW QUESTION # 91
You were asked to write python code to stop all running streams, which of the following command can be used to get a list of all active streams currently running so we can stop them, fill in the blank.
1.for s in _______________:
2. s.stop()
- A. activeStreams()
- B. getActiveStreams()
- C. Spark.getActiveStreams()
- D. spark.streams.active
- E. spark.streams.getActive
Answer: D
NEW QUESTION # 92
How do you access or use tables in the unity catalog?
- A. catalog_name.database_name.schema_name.table_name
- B. catalog_name.table_name
- C. schema_name.table_name
- D. schema_name.catalog_name.table_name
- E. catalog_name.schema_name.table_name
Answer: E
Explanation:
Explanation
The answer is catalog_name.schema_name.table_name
Graphical user interface, diagram Description automatically generated
Note: Database and Schema are analogous they are interchangeably used in the Unity catalog.
FYI, A catalog is registered under a metastore, by default every workspace has a default metastore called hive_metastore, with a unity catalog you have the ability to create meatstores and share that across multiple workspaces.
Diagram Description automatically generated
NEW QUESTION # 93
Which of the following statements can be used to test the functionality of code to test number of rows in the table equal to 10 in python?
row_count = spark.sql("select count(*) from table").collect()[0][0]
- A. assert row_count == 10, "Row count did not match"
- B. assert (row_count = 10, "Row count did not match")
- C. assert if row_count == 10, "Row count did not match"
- D. assert row_count = 10, "Row count did not match"
- E. assert if (row_count = 10, "Row count did not match")
Answer: A
Explanation:
Explanation
The answer is assert row_count == 10, "Row count did not match"
Review below documentation
NEW QUESTION # 94
You are looking to process the data based on two variables, one to check if the department is supply chain or check if process flag is set to True
- A. if department == "supply chain" or process:
- B. if department == "supply chain" | process == TRUE:
- C. if department == "supply chain" | if process == TRUE:
- D. if department = "supply chain" | process:
- E. if department == "supply chain" or process = TRUE:
Answer: A
NEW QUESTION # 95
An engineering manager uses a Databricks SQL query to monitor their team's progress on fixes related to
customer-reported bugs. The manager checks the results of the query every day, but they are manually
rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are up-dated each
day?
- A. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL
- B. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL
- C. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL
- D. They can schedule the query to run every 12 hours from the Jobs UI
- E. They can schedule the query to run every 1 day from the Jobs UI
Answer: C
NEW QUESTION # 96
What are the different ways you can schedule a job in Databricks workspace?
- A. Continuous, Incremental
- B. Once, Continuous
- C. On-Demand runs, File notification from Cloud object storage
- D. Cron, On Demand runs
- E. Cron, File notification from Cloud object storage
Answer: D
Explanation:
Explanation
The answer is, Cron, On-Demand runs
Supports running job immediately or using can be scheduled using CRON syntax
NEW QUESTION # 97
A data engineer has set up a notebook to automatically process using a Job. The data engineer's manager wants
to version control the schedule due to its complexity.
Which of the following approaches can the data engineer use to obtain a version-controllable con-figuration of
the Job's schedule?
- A. They can download the JSON description of the Job from the Job's page
- B. They can link the Job to notebooks that are a part of a Databricks Repo
- C. They can submit the Job once on an all-purpose cluster
- D. They can download the XML description of the Job from the Job's page
- E. They can submit the Job once on a Job cluster
Answer: A
NEW QUESTION # 98
A data engineering team is in the process of converting their existing data pipeline to utilize Auto Loader for
incremental processing in the ingestion of JSON files. One data engineer comes across the following code
block in the Auto Loader documentation:
1. (streaming_df = spark.readStream.format("cloudFiles")
2. .option("cloudFiles.format", "json")
3. .option("cloudFiles.schemaLocation", schemaLocation)
4. .load(sourcePath))
Assuming that schemaLocation and sourcePath have been set correctly, which of the following changes does
the data engineer need to make to convert this code block to use Auto Loader to ingest the data?
- A. There is no change required. The inclusion of format("cloudFiles") enables the use of Auto Loader
- B. The data engineer needs to change the format("cloudFiles") line to format("autoLoader")
- C. The data engineer needs to add the .autoLoader line before the .load(sourcePath) line
- D. There is no change required. The data engineer needs to ask their administrator to turn on Auto Loader
- E. There is no change required. Databricks automatically uses Auto Loader for streaming reads
Answer: A
NEW QUESTION # 99
You were asked to create a notebook that can take department as a parameter and process the data accordingly, which is the following statements result in storing the notebook parameter into a py-thon variable
- A. SET department = dbutils.widget.get("department")
- B. department = dbutils.widget.get("department")
- C. department = notebook.widget.get("department")
- D. department = notebook.param.get("department")
- E. ASSIGN department == dbutils.widget.get("department")
Answer: B
Explanation:
Explanation
The answer is department = dbutils.widget.get("department")
Refer to additional documentation here
https://docs.databricks.com/notebooks/widgets.html
NEW QUESTION # 100
......
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