PDF DA0-001 VCE | DA0-001 High Passing Score

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CompTIA Data+ Certification Exam Sample Questions (Q314-Q319):

NEW QUESTION # 314
A data analyst has removed the outliers from a data set due to large variances. Which of the following central tendencies would be the best measure to use?

A. Median
B. Mean
C. Range
D. Mode

Answer: B

NEW QUESTION # 315
A data analyst has been asked to merge the tables below, first performing an INNER JOIN and then a LEFT JOIN:

Customer Table -
In-store Transactions -

Which of the following describes the number of rows of data that can be expected after performing both joins in the order stated, considering the customer table as the main table?

A. INNER: 9 rows; LEFT: 15 rows
B. INNER: 9 rows; LEFT: 6 rows
C. INNER: 6 rows; LEFT: 9 rows
D. INNER: 15 rows; LEFT: 9 rows

Answer: A

Explanation:
Explanation
An INNER JOIN returns only the rows that match the join condition in both tables. A LEFT JOIN returns all the rows from the left table, and the matched rows from the right table, or NULL if there is no match. In this case, the customer table is the left table and the in-store transactions table is the right table. The join condition is based on the customer_id column, which is common in both tables.
To perform an INNER JOIN, we can use the following SQL query:
SELECT * FROM customer INNER JOIN in_store_transactions ON customer.customer_id = in_store_transactions.customer_id; This query will return 9 rows of data, as shown below:
customer_id | name | lastname | gender | marital_status | transaction_id | amount | date 1 | MARC | TESCO | M
| Y | 1 | 1000 | 2020-01-01 1 | MARC | TESCO | M | Y | 2 | 5000 | 2020-01-02 2 | ANNA | MARTIN | F | N | 3 |
2000 | 2020-01-03 2 | ANNA | MARTIN | F | N | 4 | 3000 | 2020-01-04 3 | EMMA | JOHNSON | F | Y | 5 |
4000 | 2020-01-05 4 | DARIO | PENTAL | M | N | 6 | 5000 | 2020-01-06 5 | ELENA | SIMSON| F| N|7|6000|2020-01-07 6|TIM|ROBITH|M|N|8|7000|2020-01-08 7|MILA|MORRIS|F|N|9|8000|2020-01-09 To perform a LEFT JOIN, we can use the following SQL query:
SELECT * FROM customer LEFT JOIN in_store_transactions ON customer.customer_id = in_store_transactions.customer_id; This query will return 15 rows of data, as shown below:
customer_id|name|lastname|gender|marital_status|transaction_id|amount|date
1|MARC|TESCO|M|Y|1|1000|2020-01-01 1|MARC|TESCO|M|Y|2|5000|2020-01-02
2|ANNA|MARTIN|F|N|3|2000|2020-01-03 2|ANNA|MARTIN|F|N|4|3000|2020-01-04
3|EMMA|JOHNSON|F|Y|5|4000|2020-01-05 4|DARIO|PENTAL|M|N|6|5000|2020-01-06
5|ELENA|SIMSON||F||N||7||6000||2020-01-07 6||TIM||ROBITH||M||N||8||7000||2020-01-08
7||MILA||MORRIS||F||N||9||8000||2020-01-09 8||JENNY||DWARTH||F||Y||NULL||NULL||NULL As you can see, the customers who do not have any transactions (customer_id = 8) are still included in the result, but with NULL values for the transaction_id, amount, and date columns.
Therefore, the correct answer is C: INNER: 9 rows; LEFT: 15 rows.

NEW QUESTION # 316
Which of the following programming languages are best suited for analysis and machine-learning applications? (Select two).

A. Rust
B. Kotlin
C. Ruby
D. PHP
E. R
F. Python

Answer: E,F

NEW QUESTION # 317
An analyst is working with the income data of suburban families in the United States. The data set has a lot of outliers, and the analyst needs to provide a measure that represents the typical income. Which of the following would BEST fulfill the analyst's goal?

A. Mean
B. Median
C. Standard deviation
D. Mode

Answer: B

Explanation:
his is because median is a type of statistical measure that represents the typical value or central tendency of a data set, which means that it divides the data set into two equal halves, such that half of the values are above it and half are below it. Median can be used to provide a measure that represents the typical income of suburban families in the United States, especially when the data set has a lot of outliers, which means that it has values that are unusually high or low compared to the rest of the data set. Median can provide a measure that represents the typical income of suburban families in the United States, because it is not affected or skewed by the outliers, as it only depends on the middle value or the middle two values of the data set, regardless of how extreme or distant the outliers are. For example, median can provide a measure that represents the typical income of suburban families in the United States, by finding the income value that splits the data set into two equal groups of families, such that 50% of the families have higher incomes and 50% have lower incomes. The other statistical measures are not the best measures to represent the typical income of suburban families in the United States. Here is why:
Mean is a type of statistical measure that represents the average value or central tendency of a data set, which means that it is the sum of all the values divided by the number of values. Mean is not a good measure to represent the typical income of suburban families in the United States, especially when the data set has a lot of outliers, because it is affected or skewed by the outliers, as it takes into account all the values in the data set, regardless of how extreme or distant they are. For example, mean can provide a measure that does not represent the typical income of suburban families in the United States, by finding the income value that is influenced by a few very high or very low incomes, which could make it higher or lower than most of the incomes in the data set.
Mode is a type of statistical measure that represents the most frequent value or mode of a data set, which means that it is the value that occurs most often in the data set. Mode is not a good measure to represent the typical income of suburban families in the United States, especially when the data set has a lot of outliers, because it is not representative or indicative of the central tendency or distribution of the data set, as it only depends on the count or occurrence of a single value or a few values in the data set, regardless of how common or rare they are. For example, mode can provide a measure that does not represent the typical income of suburban families in the United States, by finding the income value that is repeated more often than others, which could be an outlier or an anomaly in the data set.
Standard deviation is a type of statistical measure that represents the amount of dispersion or variation of a data set, which means that it quantifies how much the values in a data set vary or deviate from the mean or average of the data set. Standard deviation is not a measure that represents the typical income of suburban families in the United States, but rather a measure that describes the spread or distribution of their incomes, as well as identifies any outliers or extreme values in their incomes. For example, standard deviation can provide a measure that describes how diverse or homogeneous their incomes are, as well as how far their incomes are from their average income.

NEW QUESTION # 318
Joe. an analyst. tests the loading time on a dashboard he is preparing to go live and finds it is slower than he would like. Which of the following must occur to decrease the loading time?

A. Change the field definitions.
B. Optimize the dashboard.
C. Update the dashboard subscribers.
D. Deploy the dashboard to production.

Answer: B

Explanation:
Explanation
Optimizing the dashboard is the process of improving its performance and reducing its loading time by applying various techniques and best practices. Some of the common ways to optimize a dashboard are:
Reducing the size and complexity of the data model, such as removing unnecessary columns, aggregating data at the source, or using data compression techniques12 Leveraging caching strategies, such as setting appropriate cache refresh intervals or utilizing Power BI's built-in caching mechanisms, to minimize data retrieval delays2 Utilizing query folding, direct query, or live connection to enhance data processing efficiency and enable real-time data updates23 Optimizing DAX queries, such as avoiding nested calculations, using variables, or simplifying measures, to improve data calculation speed23 Reducing visualizations and calculations, such as using fewer or simpler charts, filters, or parameters, to speed up dashboard rendering12 Evaluating the impact of custom visuals on dashboard load time and avoiding or replacing those that are slow or inefficient2 Applying aggregation and summarization techniques, such as using extract filters, context filters, or level of detail expressions, to reduce the amount of data displayed on the dashboard1 Troubleshooting and resolving any issues that may cause slow dashboard load, such as network latency, server overload, or hardware limitations24

NEW QUESTION # 319
......

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CompTIA Data+ Certification Exam Sample Questions (Q314-Q319):

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