Dec 17, 2022 Updated Databricks-Certified-Professional-Data-Engineer Dumps Questions For Databricks Exam [Q32-Q46]

Rate this post

Dec 17, 2022 Updated Databricks-Certified-Professional-Data-Engineer Dumps Questions For Databricks Exam

Best Value Available Preparation Guide for Databricks-Certified-Professional-Data-Engineer Exam

NO.32 Which of the following is a Continuous Probability Distributions?

Binomial probability distribution

Negative binomial distribution

Poisson probability distribution

Normal probability distribution

NO.33 A data analyst has noticed that their Databricks SQL queries are running too slowly. They claim that this issue
is affecting all of their sequentially run queries. They ask the data engineering team for help. The data
engineering team notices that each of the queries uses the same SQL endpoint, but the SQL endpoint is not
used by any other user.
Which of the following approaches can the data engineering team use to improve the latency of the data
analyst’s queries?

They can increase the maximum bound of the SQL endpoint’s scaling range

They can increase the cluster size of the SQL endpoint

They can turn on the Auto Stop feature for the SQL endpoint

They can turn on the Serverless feature for the SQL endpoint and change the Spot In-stance Policy to
“Reliability Optimized”

They can turn on the Serverless feature for the SQL endpoint

NO.34 A new data engineer [email protected] has been assigned to an ELT project. The new data
engineer will need full privileges on the table sales to fully manage the project.
Which of the following commands can be used to grant full permissions on the table to the new data engineer?

1. GRANT ALL PRIVILEGES ON TABLE [email protected] TO sales;

1. GRANT SELECT ON TABLE sales TO [email protected];

1. GRANT ALL PRIVILEGES ON TABLE sales TO [email protected];

1. GRANT USAGE ON TABLE sales TO [email protected];

1. GRANT SELECT CREATE MODIFY ON TABLE sales TO [email protected];

NO.35 You are working on a email spam filtering assignment, while working on this you find there is new word e.g.
HadoopExam comes in email, and in your solutions you never come across this word before, hence probability
of this words is coming in either email could be zero. So which of the following algorithm can help you to
avoid zero probability?

Naive Bayes

Laplace Smoothing

Logistic Regression

All of the above

NO.36 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?

There is no change required. The inclusion of format(“cloudFiles”) enables the use of Auto Loader

There is no change required. Databricks automatically uses Auto Loader for streaming reads

The data engineer needs to change the format(“cloudFiles”) line to format(“autoLoader”)

The data engineer needs to add the .autoLoader line before the .load(sourcePath) line

There is no change required. The data engineer needs to ask their administrator to turn on Auto Loader

NO.37 Which of the following Structured Streaming queries is performing a hop from a Bronze table to a Silver
table?

1. (spark.table(“sales”)
2. .groupBy(“store”)
3. .agg(sum(“sales”))
4. .writeStream
5. .option(“checkpointLocation”, checkpointPath)
6. .outputMode(“complete”)
7. .table(“aggregatedSales”)
8.)

1. (spark.table(“sales”)
2. .withColumn(“avgPrice”, col(“sales”) / col(“units”))
3. .writeStream
4. .option(“checkpointLocation”, checkpointPath)
5. .outputMode(“append”)
6. .table(“cleanedSales”)
7.)

1. (spark.readStream.load(rawSalesLocation)
2. .writeStream
3. .option(“checkpointLocation”, checkpointPath)
4. .outputMode(“append”)
5. .table(“uncleanedSales”)
6. )

1. (spark.table(“sales”)
2. .agg(sum(“sales”),
3. sum(“units”))
4. .writeStream
5. .option(“checkpointLocation”, checkpointPath)
6. .outputMode(“complete”)
7. .table(“aggregatedSales”)
8. )

1. (spark.read.load(rawSalesLocation)
2. .writeStream
3. .option(“checkpointLocation”, checkpointPath)
4. .outputMode(“append”)
5. .table(“uncleanedSales”)
6. )

NO.38 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?

1.SELECT transaction_id, date from payload
2.FROM raw_table;

1.SELECT transaction_id, explode(payload)
2.FROM raw_table;

1.SELECT transaction_id, payload[date]
2.FROM raw_table;

1.SELECT transaction_id, date
2.FROM raw_table;

1.SELECT transaction_id, payload.date
2.FROM raw_table;

NO.39 A data engineer has set up two Jobs that each run nightly. The first Job starts at 12:00 AM, and it usually
completes in about 20 minutes. The second Job depends on the first Job, and it starts at 12:30 AM. Sometimes,
the second Job fails when the first Job does not complete by 12:30 AM.
Which of the following approaches can the data engineer use to avoid this problem?

They can set up a retry policy on the first Job to help it run more quickly

They can use cluster pools to help the Jobs run more efficiently

They can limit the size of the output in the second Job so that it will not fail as easily

They can set up the data to stream from the first Job to the second Job

They can utilize multiple tasks in a single job with a linear dependency

NO.40 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?

1. CREATE MANAGED TABLE my_table (id STRING, value STRING) USING
2. org.apache.spark.sql.parquet OPTIONS (PATH “storage-path”);

1. CREATE TABLE my_table (id STRING, value STRING) USING DBFS;

1. CREATE TABLE my_table (id STRING, value STRING) USING
2. org.apache.spark.sql.parquet OPTIONS (PATH “storage-path”)

1. CREATE TABLE my_table (id STRING, value STRING);

1. CREATE MANAGED TABLE my_table (id STRING, value STRING);

NO.41 A dataset has been defined using Delta Live Tables and includes an expectations clause:
1. CONSTRAINT valid_timestamp EXPECT (timestamp > ‘2020-01-01’)
What is the expected behaviour when a batch of data containing data that violates these constraints is
processed?

Records that violate the expectation cause the job to fail

Records that violate the expectation are added to the target dataset and flagged as in-valid in a field added to the target dataset

Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table

Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log

Records that violate the expectation are added to the target dataset and recorded as invalid in the event log

NO.42 A data engineer has developed a code block to perform a streaming read on a data source. The code block is
below:
1. (spark
2. .read
3. .schema(schema)
4. .format(“cloudFiles”)
5. .option(“cloudFiles.format”, “json”)
6. .load(dataSource)
7. )
The code block is returning an error.
Which of the following changes should be made to the code block to configure the block to successfully
perform a streaming read?

The .read line should be replaced with .readStream

The .format(“cloudFiles”) line should be replaced with .format(“stream”)

A new .stream line should be added after the spark line

A new .stream line should be added after the .read line

A new .stream line should be added after the .load(dataSource) line

NO.43 A data engineer needs to dynamically create a table name string using three Python varia-bles: region, store,
and year. An example of a table name is below when region = “nyc”, store = “100”, and year = “2021”:
nyc100_sales_2021
Which of the following commands should the data engineer use to construct the table name in Py-thon?

“{region}+{store}+_sales_+{year}”

“{region}+{store}+”_sales_”+{year}”

f”{region}+{store}+_sales_+{year}”

f”{region}{store}_sales_{year}”

“{region}{store}_sales_{year}”

NO.44 Which of the following data workloads will utilize a Bronze table as its source?

A job that queries aggregated data to publish key insights into a dashboard

A job that ingests raw data from a streaming source into the Lakehouse

A job that enriches data by parsing its timestamps into a human-readable format

A job that develops a feature set for a machine learning application

A job that aggregates cleaned data to create standard summary statistics

NO.45 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?

They can download the JSON description of the Job from the Job’s page

They can submit the Job once on an all-purpose cluster

They can link the Job to notebooks that are a part of a Databricks Repo

They can submit the Job once on a Job cluster

They can download the XML description of the Job from the Job’s page

NO.46 A data architect is designing a data model that works for both video-based machine learning work-loads and
highly audited batch ETL/ELT workloads.
Which of the following describes how using a data lakehouse can help the data architect meet the needs of
both workloads?

A data lakehouse requires very little data modeling

A data lakehouse combines compute and storage for simple governance

A data lakehouse fully exists in the cloud

A data lakehouse stores unstructured data and is ACID-compliant

A data lakehouse provides autoscaling for compute clusters