Databricks-Certified-Data-Analyst-Associate Study Test, Databricks-Certified-Data-Analyst-Associate Authorized Exam Dumps

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Databricks Databricks-Certified-Data-Analyst-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	Data Management: The topic describes Delta Lake as a tool for managing data files, Delta Lake manages table metadata, benefits of Delta Lake within the Lakehouse, tables on Databricks, a table owner’s responsibilities, and the persistence of data. It also identifies management of a table, usage of Data Explorer by a table owner, and organization-specific considerations of PII data. Lastly, the topic it explains how the LOCATION keyword changes, usage of Data Explorer to secure data.
Topic 2	Databricks SQL: This topic discusses key and side audiences, users, Databricks SQL benefits, complementing a basic Databricks SQL query, schema browser, Databricks SQL dashboards, and the purpose of Databricks SQL endpoints warehouses. Furthermore, the delves into Serverless Databricks SQL endpoint warehouses, trade-off between cluster size and cost for Databricks SQL endpoints warehouses, and Partner Connect. Lastly it discusses small-file upload, connecting Databricks SQL to visualization tools, the medallion architecture, the gold layer, and the benefits of working with streaming data.
Topic 3	SQL in the Lakehouse: It identifies a query that retrieves data from the database, the output of a SELECT query, a benefit of having ANSI SQL, access, and clean silver-level data. It also compares and contrasts MERGE INTO, INSERT TABLE, and COPY INTO. Lastly, this topic focuses on creating and applying UDFs in common scaling scenarios.
Topic 4	Data Visualization and Dashboarding: Sub-topics of this topic are about of describing how notifications are sent, how to configure and troubleshoot a basic alert, how to configure a refresh schedule, the pros and cons of sharing dashboards, how query parameters change the output, and how to change the colors of all of the visualizations. It also discusses customized data visualizations, visualization formatting, Query Based Dropdown List, and the method for sharing a dashboard.
Topic 5	Analytics applications: It describes key moments of statistical distributions, data enhancement, and the blending of data between two source applications. Moroever, the topic also explains last-mile ETL, a scenario in which data blending would be beneficial, key statistical measures, descriptive statistics, and discrete and continuous statistics.

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Databricks Certified Data Analyst Associate Exam Sample Questions (Q69-Q74):

NEW QUESTION # 69
How can a data analyst determine if query results were pulled from the cache?

A. Go to the Queries tab and click on Cache Status. The status will be green if the results from the last run came from the cache.
B. Go to the Data tab and click Last Query. The details of the query will show if the results came from the cache.
C. Go to the SQL Warehouse (formerly SQL Endpoints) tab and click on Cache. The Cache file will show the contents of the cache.
D. Go to the Query History tab and click on the text of the query. The slideout shows if the results came from the cache.
E. Go to the Alerts tab and check the Cache Status alert.

Answer: D

Explanation:
Databricks SQL uses a query cache to store the results of queries that have been executed previously. This improves the performance and efficiency of repeated queries. To determine if a query result was pulled from the cache, you can go to the Query History tab in the Databricks SQL UI and click on the text of the query. A slideout will appear on the right side of the screen, showing the query details, including the cache status. If the result came from the cache, the cache status will show "Cached". If the result did not come from the cache, the cache status will show "Not cached". You can also see the cache hit ratio, which is the percentage of queries that were served from the cache. References: The answer can be verified from Databricks SQL documentation which provides information on how to use the query cache and how to check the cache status.
Reference link: Databricks SQL - Query Cache

NEW QUESTION # 70
A data analyst is working with a nested array column products in table transactions. The analyst wants to return the first item in the array for each row.
The data analyst is using the following incomplete command:
SELECT
transaction_id,
_____ AS first_product
FROM transactions;
Which line of code should the data analyst use to fill in the blank so that it successfully completes the task?

A. products.1
B. products[0]
C. products.0
D. products[1]

Answer: B

Explanation:
The correct answer is C because Databricks SQL supports bracket notation for arrays, and array indexing starts at 0. Therefore, the first element of the products array is accessed with products[0]. Options A and B use dot notation, which is not the correct syntax for array indexing. Option D returns the second element, not the first.
Official documentation extract used: Databricks states that the bracket operator returns an array element by index and that "the first element of an ARRAY is at index 0."

NEW QUESTION # 71
A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

A. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
B. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
C. They can reduce the cluster size of the SQL endpoint.
D. They can set up the dashboard's SQL endpoint to be serverless.
E. They can turn on the Auto Stop feature for the SQL endpoint.

Answer: E

Explanation:
Option C is correct because Auto Stop is specifically designed to stop a SQL warehouse when it has been idle for a configured number of minutes. This minimizes total warehouse running time after the scheduled dashboard refresh completes. Reducing cluster size may reduce hourly cost but does not stop the warehouse.
Serverless can help with management, but the feature directly matching "only running when necessary" is Auto Stop. Official Databricks extract: "Auto Stop determines whether the warehouse stops if it's idle for the specified number of minutes." Databricks also notes that idle SQL warehouses continue to accumulate charges until stopped.

NEW QUESTION # 72
A Data Analyst is working on employees_df and needs to add a new column where a 10% tax is calculated on the salary. Additionally, the DataFrame contains the column age, which is not needed.
Which code fragment adds the tax column and removes the age column?

A. employees_df = employees_df.withColumn( " tax " , employees_df.salary * 10).dropField( " age " )
B. employees_df = employees_df.withColumn( " tax " , employees_df.salary * 10).drop( " age " )
C. employees_df = employees_df.withColumn( " tax " , employees_df.salary * 0.1).drop( " age " )
D. employees_df = employees_df.withColumn( " tax " , lit(0.1) * col( " salary " )).dropField( " age " )

Answer: C

Explanation:
Option B is correct after correcting the uploaded typo withcolumn to withColumn. A 10% tax is calculated by multiplying salary by 0.1, not by 10. The age column should be removed using .drop( " age " ). Options A and D multiply salary by 10, which calculates 1000%, not 10%. Options A and C also use dropField, which is not the correct DataFrame method for removing a top-level column. Official Databricks PySpark documentation states that withColumn adds or replaces a column and that drop(*cols) returns a new DataFrame without the specified columns.

NEW QUESTION # 73
In which of the following situations will the mean value and median value of variable be meaningfully different?

A. When the variable is of the categorical type
B. When the variable contains no missing values
C. When the variable contains no outliers
D. When the variable is of the boolean type
E. When the variable contains a lot of extreme outliers

Answer: E

Explanation:
The mean value of a variable is the average of all the values in a data set, calculated by dividing the sum of the values by the number of values. The median value of a variable is the middle value of the ordered data set, or the average of the middle two values if the data set has an even number of values. The mean value is sensitive to outliers, which are values that are very different from the rest of the data. Outliers can skew the mean value and make it less representative of the central tendency of the data. The median value is more robust to outliers, as it only depends on the middle values of the data. Therefore, when the variable contains a lot of extreme outliers, the mean value and the median value will be meaningfully different, as the mean value will be pulled towards the outliers, while the median value will remain close to the majority of the data1. References: Difference Between Mean and Median in Statistics (With Example) - BYJU'S

NEW QUESTION # 74
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