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NEW QUESTION 26
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 Regression
• B. Laplace Smoothing
• C. Naive Bayes
• D. Linear Regression

Answer: B

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 27
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. Logistic Regression
• B. Laplace Smoothing
• C. Naive Bayes
• D. All of the above

Answer: B

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 28
Two junior data engineers are authoring separate parts of a single data pipeline notebook. They are working on
separate Git branches so they can pair program on the same notebook simultaneously. A senior data engineer
experienced in Databricks suggests there is a better alternative for this type of collaboration.
Which of the following supports the senior data engineer's claim?

• A. Databricks Notebooks support the use of multiple languages in the same notebook
• B. Databricks Notebooks support real-time co-authoring on a single notebook
• C. Databricks Notebooks support the creation of interactive data visualizations
• D. Databricks Notebooks support automatic change-tracking and versioning
• E. Databricks Notebooks support commenting and notification comments

Answer: B

NEW QUESTION 29
Which of the following data workloads will utilize a Bronze table as its source?

• A. A job that queries aggregated data to publish key insights into a dashboard
• B. A job that enriches data by parsing its timestamps into a human-readable format
• C. A job that ingests raw data from a streaming source into the Lakehouse
• D. A job that develops a feature set for a machine learning application
• E. A job that aggregates cleaned data to create standard summary statistics

Answer: B

NEW QUESTION 30
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 Data page
• B. They need to refactor their notebook to use SQL and CREATE LIVE TABLE keyword
• C. They need to create a Delta Live Tables pipeline from the Jobs page
• D. They need to create a Delta Live tables pipeline from the Compute page
• E. They need to refactor their notebook to use Python and the dlt library

Answer: C

NEW QUESTION 31
......

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