[2022] Databricks-Certified-Professional-Data-Engineer Answers Databricks-Certified-Professional-Data-Engineer Free Demo Are Based On The Real Exam
Databricks-Certified-Professional-Data-Engineer [Aug-2022 Newly Released] Exam Questions For You To Pass
NEW QUESTION 36
A data engineering manager has noticed that each of the queries in a Databricks SQL dashboard takes a few
minutes to update when they manually click the "Refresh" button. They are curious why this might be
occurring, so a team member provides a variety of reasons on why the delay might be occurring.
Which of the following reasons fails to explain why the dashboard might be taking a few minutes to update?
- A. The queries attached to the dashboard might all be connected to their own, unstarted Databricks clusters
- B. The queries attached to the dashboard might take a few minutes to run under normal circumstances
- C. The queries attached to the dashboard might first be checking to determine if new data is available
- D. The SQL endpoint being used by each of the queries might need a few minutes to start up
- E. The Job associated with updating the dashboard might be using a non-pooled endpoint
Answer: E
NEW QUESTION 37
Which of the following benefits does Delta Live Tables provide for ELT pipelines over standard data pipelines
that utilize Spark and Delta Lake on Databricks?
- A. The ability to declare and maintain data table dependencies
- B. The ability to write pipelines in Python and/or SQL
- C. The ability to automatically scale compute resources
- D. The ability to access previous versions of data tables
- E. The ability to perform batch and streaming queries
Answer: A
NEW QUESTION 38
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. There is no change required. Databricks automatically uses Auto Loader for streaming reads
- C. There is no change required. The data engineer needs to ask their administrator to turn on Auto Loader
- D. The data engineer needs to add the .autoLoader line before the .load(sourcePath) line
- E. The data engineer needs to change the format("cloudFiles") line to format("autoLoader")
Answer: A
NEW QUESTION 39
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. A data lakehouse stores unstructured data and is ACID-compliant
- B. A data lakehouse fully exists in the cloud
- C. A data lakehouse combines compute and storage for simple governance
- D. A data lakehouse provides autoscaling for compute clusters
- E. A data lakehouse requires very little data modeling
Answer: A
NEW QUESTION 40
Suppose there are three events then which formula must always be equal to P(E1|E2,E3)?
- A. P(E1,E2|E3)P(E3)
- B. P(E1,E2,E3)P(E2)P(E3)
- C. P(E1,E2|E3)P(E2|E3)P(E3)
- D. P(E1,E2,E3)P(E1)/P(E2:E3)
- E. P(E1,E2;E3)/P(E2,E3)
Answer: E
Explanation:
Explanation
This is an application of conditional probability: P(E1,E2)=P(E1|E2)P(E2). so
P(E1|E2) = P(E1.E2)/P(E2)
P(E1,E2,E3)/P(E2,E3)
If the events are A and B respectively, this is said to be "the probability of A given B"
It is commonly denoted by P(A|B):or sometimes PB(A). In case that both "A" and "B" are categorical
variables, conditional probability table is typically used to represent the conditional probability.
NEW QUESTION 41
Projecting a multi-dimensional dataset onto which vector has the greatest variance?
- A. first eigenvector
- B. second eigenvector
- C. first principal component
- D. not enough information given to answer
- E. second principal component
Answer: C
Explanation:
Explanation
The method based on principal component analysis (PCA) evaluates the features according to the projection of
the largest eigenvector of the correlation matrix on the initial dimensions, the method based on Fisher's linear
discriminant analysis evaluates. Them according to the magnitude of the components of the discriminant
vector.
The first principal component corresponds to the greatest variance in the data, by definition. If we project the
data onto the first principal component line, the data is more spread out (higher variance) than if projected onto
any other line, including other principal components.
NEW QUESTION 42
Consider flipping a coin for which the probability of heads is p, where p is unknown, and our goa is to
estimate p. The obvious approach is to count how many times the coin came up heads and divide by the total
number of coin flips. If we flip the coin 1000 times and it comes up heads 367 times, it is very reasonable to
estimate p as approximately 0.367. However, suppose we flip the coin only twice and we get heads both times.
Is it reasonable to estimate p as 1.0? Intuitively, given that we only flipped the coin twice, it seems a bit
rash to conclude that the coin will always come up heads, and____________is a way of avoiding such rash
conclusions.
- A. Laplace Smoothing
- B. Linear Regression
- C. Naive Bayes
- D. Logistic Regression
Answer: A
Explanation:
Explanation
Smooth the estimates:consider flipping a coin for which the probability of heads is p, where p is unknown, and
our goal is to estimate p. The obvious approach is to count how many times the coin came up heads and divide
by the total number of coin flips. If we flip the coin 1000 times and it comes up heads 367 times, it is very
reasonable to estimate p as approximately 0.367. However, suppose we flip the coin only twice and we get
heads both times. Is it reasonable to estimate p as 1.0? Intuitively, given that we only flipped the coin twice, it
seems a bit rash to conclude that the coin will always come up heads, and smoothing is a way of avoiding such
rash conclusions. A simple smoothing method, called Laplace smoothing (or Laplace's law of succession or
add-one smoothing in R&N), is to estimate p by (one plus the number of heads) / (two plus the total number of
flips). Said differently, if we are keeping count of the number of heads and the number of tails, this rule is
equivalent to starting each of our counts at one, rather than zero. Another advantage of Laplace smoothing is
that it avoids estimating any probabilities to be zero, even for events never observed in the data. Laplace
add-one smoothing now assigns too much probability to unseen words
NEW QUESTION 43
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?
- A. A new .stream line should be added after the spark line
- B. The .read line should be replaced with .readStream
- C. A new .stream line should be added after the .load(dataSource) line
- D. A new .stream line should be added after the .read line
- E. The .format("cloudFiles") line should be replaced with .format("stream")
Answer: B
NEW QUESTION 44
A data engineer has a Job with multiple tasks that runs nightly. One of the tasks unexpectedly fails during 10
percent of the runs.
Which of the following actions can the data engineer perform to ensure the Job completes each night while
minimizing compute costs?
- A. They can institute a retry policy for the task that periodically fails
- B. They can utilize a Jobs cluster for each of the tasks in the Job
- C. They can set up the Job to run multiple times ensuring that at least one will complete
- D. They can observe the task as it runs to try and determine why it is failing
- E. They can institute a retry policy for the entire Job
Answer: A
NEW QUESTION 45
You are asked to create a model to predict the total number of monthly subscribers for a specific magazine.
You are provided with 1 year's worth of subscription and payment data, user demographic data, and 10 years
worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building
a predictive model for subscribers?
- A. Logistic regression
- B. Linear regression
- C. TF-IDF
- D. Decision trees
Answer: B
NEW QUESTION 46
A new data engineer has started at a company. The data engineer has recently been added to the company's
Databricks workspace as [email protected]. The data engineer needs to be able to query the table
sales in the database retail. The new data engineer already has been granted USAGE on the database retail.
Which of the following commands can be used to grant the appropriate permissions to the new data engineer?
- A. GRANT SELECT ON TABLE [email protected] TO sales;
- B. GRANT USAGE ON TABLE sales TO [email protected];
- C. GRANT CREATE ON TABLE sales TO [email protected];
- D. GRANT SELECT ON TABLE sales TO [email protected];
- E. GRANT USAGE ON TABLE [email protected] TO sales;
Answer: D
NEW QUESTION 47
Which of the following data workloads will utilize a Silver table as its source?
- A. A job that aggregates cleaned data to create standard summary statistics
- B. A job that cleans data by removing malformatted records
- C. A job that ingests raw data from a streaming source into the Lakehouse
- D. A job that queries aggregated data that already feeds into a dashboard
- E. A job that enriches data by parsing its timestamps into a human-readable format
Answer: A
NEW QUESTION 48
A data engineer has ingested data from an external source into a PySpark DataFrame raw_df. They need to
briefly make this data available in SQL for a data analyst to perform a quality assurance check on the data.
Which of the following commands should the data engineer run to make this data available in SQL for only
the remainder of the Spark session?
- A. raw_df.createTable("raw_df")
- B. There is no way to share data between PySpark and SQL
- C. raw_df.saveAsTable("raw_df")
- D. raw_df.write.save("raw_df")
- E. raw_df.createOrReplaceTempView("raw_df")
Answer: E
NEW QUESTION 49
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?
- A. Laplace Smoothing
- B. Naive Bayes
- C. All of the above
- D. Logistic Regression
Answer: A
Explanation:
Explanation
Laplace smoothing is a technique for parameter estimation which accounts for unobserved events. It is more
robust and will not fail completely when data that has never been observed in training shows up.
NEW QUESTION 50
Which of the following is a Continuous Probability Distributions?
- A. Poisson probability distribution
- B. Binomial probability distribution
- C. Normal probability distribution
- D. Negative binomial distribution
Answer: C
NEW QUESTION 51
A data engineer has three notebooks in an ELT pipeline. The notebooks need to be executed in a specific order
for the pipeline to complete successfully. The data engineer would like to use Delta Live Tables to manage this
process.
Which of the following steps must the data engineer take as part of implementing this pipeline using Delta
Live Tables?
- A. They need to create a Delta Live tables pipeline from the Compute page
- B. They need to refactor their notebook to use Python and the dlt library
- C. They need to refactor their notebook to use SQL and CREATE LIVE TABLE keyword
- D. They need to create a Delta Live Tables pipeline from the Jobs page
- E. They need to create a Delta Live Tables pipeline from the Data page
Answer: D
NEW QUESTION 52
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