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DY0-001 Short Quiz
5 Real Exam Questions - One at a Time - Instant or End-of-quiz Scoring.
A data analyst wants to generate the most data using tables from a database. Which of the following is the best way to accomplish this objective?
A full outer join returns every row from both tables, matched where possible and unmatched rows filled with NULLs, yielding at least as many (and typically more) rows than any other join type.
A data scientist needs to:
Build a predictive model that gives the likelihood that a car will get a flat tire.
Provide a data set of cars that had flat tires and cars that did not.
All the cars in the data set had sensors taking weekly measurements of tire pressure similar to the sensors that will be installed in the cars consumers drive. Which of the following is the most immediate data concern?
Because tire-pressure sensors report only weekly measurements, you risk missing the critical pressure drop immediately preceding a flat. Those stale (''lagged'') readings may not reflect the condition just before failure, undermining your model's ability to learn the true precursors to a flat tire.
A data scientist needs to analyze a company's chemical businesses and is using the master database of the conglomerate company. Nothing in the data differentiates the data observations for the different businesses. Which of the following is the most efficient way to identify the chemical businesses' observations?
Engaging the business team leverages domain expertise to pinpoint which records pertain to chemical operations, allowing you to extract and analyze just the relevant subset. This avoids the time and resource waste of ingesting and sifting through unrelated data.
Given the following:

Which of the following time series models best represents this process?
The model has a single autoregressive term and only whitenoise errors, matching the definition of an AR(1) process.
A data scientist is working with a data set that has ten predictors and wants to use only the predictors that most influence the results. Which of the following models would be the best for the data scientist to use?
LASSO regression uses an L1 penalty that drives lessimportant feature coefficients to exactly zero, effectively selecting only the predictors that most influence the outcome.
DY0-001 free sample questions and answers
CompTIA DY0-001 practice quiz with correct answers for search indexing.
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Question 1
A data analyst wants to generate the most data using tables from a database. Which of the following is the best way to accomplish this objective?
Correct answer: D
A full outer join returns every row from both tables, matched where possible and unmatched rows filled with NULLs, yielding at least as many (and typically more) rows than any other join type.
-
Question 2
A data scientist needs to:
Build a predictive model that gives the likelihood that a car will get a flat tire.
Provide a data set of cars that had flat tires and cars that did not.
All the cars in the data set had sensors taking weekly measurements of tire pressure similar to the sensors that will be installed in the cars consumers drive. Which of the following is the most immediate data concern?
Correct answer: D
Because tire-pressure sensors report only weekly measurements, you risk missing the critical pressure drop immediately preceding a flat. Those stale (''lagged'') readings may not reflect the condition just before failure, undermining your model's ability to learn the true precursors to a flat tire.
-
Question 3
A data scientist needs to analyze a company's chemical businesses and is using the master database of the conglomerate company. Nothing in the data differentiates the data observations for the different businesses. Which of the following is the most efficient way to identify the chemical businesses' observations?
Correct answer: C
Engaging the business team leverages domain expertise to pinpoint which records pertain to chemical operations, allowing you to extract and analyze just the relevant subset. This avoids the time and resource waste of ingesting and sifting through unrelated data.
-
Question 4
Given the following:

Which of the following time series models best represents this process?
Correct answer: D
The model has a single autoregressive term and only whitenoise errors, matching the definition of an AR(1) process.
-
Question 5
A data scientist is working with a data set that has ten predictors and wants to use only the predictors that most influence the results. Which of the following models would be the best for the data scientist to use?
Correct answer: D
LASSO regression uses an L1 penalty that drives lessimportant feature coefficients to exactly zero, effectively selecting only the predictors that most influence the outcome.
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