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Download Databricks Certified Professional Data Engineer Exam Exam Dumps

NEW QUESTION 29
A data engineering team has created a series of tables using Parquet data stored in an external sys-tem. The
team is noticing that after appending new rows to the data in the external system, their queries within
Databricks are not returning the new rows. They identify the caching of the previous data as the cause of this
issue.
Which of the following approaches will ensure that the data returned by queries is always up-to-date?

A. The tables should be converted to the Delta formatB. The tables should be stored in a cloud-based external systemC. The tables should be refreshed in the writing cluster before the next query is runD. The tables should be altered to include metadata to not cacheE. The tables should be updated before the next query is run

Answer: A

 

NEW QUESTION 30
Which of the following is a Continuous Probability Distributions?

A. Poisson probability distributionB. Normal probability distributionC. Negative binomial distributionD. Binomial probability distribution

Answer: B

 

NEW QUESTION 31
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. Logistic RegressionB. Naive BayesC. Laplace SmoothingD. Linear Regression

Answer: C

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 32
A data engineering team has been using a Databricks SQL query to monitor the performance of an ELT job.
The ELT job is triggered by a specific number of input records being ready to process. The Databricks SQL
query returns the number of minutes since the job's most recent runtime.
Which of the following approaches can enable the data engineering team to be notified if the ELT job has not
been run in an hour?

A. They can set up an Alert for the accompanying dashboard to notify them if the returned value is greater
than 60B. They can set up an Alert for the query to notify when the ELT job failsC. They can set up an Alert for the query to notify them if the returned value is greater than 60D. This type of alerting is not possible in DatabricksE. They can set up an Alert for the accompanying dashboard to notify when it has not re-freshed in 60
minutes

Answer: C

 

NEW QUESTION 33
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