And you just need to spend one or two days to practice Associate-Developer-Apache-Spark training questions and know your weakness and strength during the preparation, Databricks Associate-Developer-Apache-Spark Download Fee So does Online Test Engine, Databricks Associate-Developer-Apache-Spark Download Fee With the development of international technology and global integration certifications will be more and more valued, Our Associate-Developer-Apache-Spark study materials are constantly improving themselves.
Here are striking points of our Associate-Developer-Apache-Spark real questions, Creating a New Desktop, However, the way people interact in the group, and the approach to driving results, can vary with the task.
Download Associate-Developer-Apache-Spark Exam Dumps
The client has poor control of her diabetes, Animating Pivot Table Data on a Map, And you just need to spend one or two days to practice Associate-Developer-Apache-Spark training questions and know your weakness and strength during the preparation.
So does Online Test Engine, With the development of international technology and global integration certifications will be more and more valued, Our Associate-Developer-Apache-Spark study materials are constantly improving themselves.
Actual4Dumps is ready to pay back if you fail https://www.actual4dumps.com/Associate-Developer-Apache-Spark-study-material.html exam, For further detail you may contact us our customer service staff any time, To nail the Associate-Developer-Apache-Spark exam, what you need are admittedly high reputable Associate-Developer-Apache-Spark practice materials like our Associate-Developer-Apache-Spark exam questions.
Useful Associate-Developer-Apache-Spark Download Fee – Find Shortcut to Pass Associate-Developer-Apache-Spark Exam
what's more, we check the updating of Associate-Developer-Apache-Spark vce dump everyday to make sure the accuracy of questions, so you can rest assured the valid of our Associate-Developer-Apache-Spark dump torrent.
We provide the latest Associate-Developer-Apache-Spark questions answers, As long as you choose our products, the Associate-Developer-Apache-Spark latest pdf material will be able to help you pass the exam, and allow you to achieve a high level of efficiency in a short time.
If you purchase our Associate-Developer-Apache-Spark practice materials, we believe that your life will get better and better, Just as the old saying goes, success favors those people who prepare fully for something.
Download Databricks Certified Associate Developer for Apache Spark 3.0 Exam Exam Dumps
NEW QUESTION 25
Which of the following code blocks returns the number of unique values in column storeId of DataFrame transactionsDf?
- A. transactionsDf.select(distinct("storeId")).count()
- B. transactionsDf.select(count("storeId")).dropDuplicates()
- C. transactionsDf.select("storeId").dropDuplicates().count()
- D. transactionsDf.distinct().select("storeId").count()
- E. transactionsDf.dropDuplicates().agg(count("storeId"))
Answer: C
Explanation:
Explanation
transactionsDf.select("storeId").dropDuplicates().count()
Correct! After dropping all duplicates from column storeId, the remaining rows get counted, representing the number of unique values in the column.
transactionsDf.select(count("storeId")).dropDuplicates()
No. transactionsDf.select(count("storeId")) just returns a single-row DataFrame showing the number of non-null rows. dropDuplicates() does not have any effect in this context.
transactionsDf.dropDuplicates().agg(count("storeId"))
Incorrect. While transactionsDf.dropDuplicates() removes duplicate rows from transactionsDf, it does not do so taking only column storeId into consideration, but eliminates full row duplicates instead.
transactionsDf.distinct().select("storeId").count()
Wrong. transactionsDf.distinct() identifies unique rows across all columns, but not only unique rows with respect to column storeId. This may leave duplicate values in the column, making the count not represent the number of unique values in that column.
transactionsDf.select(distinct("storeId")).count()
False. There is no distinct method in pyspark.sql.functions.
NEW QUESTION 26
In which order should the code blocks shown below be run in order to assign articlesDf a DataFrame that lists all items in column attributes ordered by the number of times these items occur, from most to least often?
Sample of DataFrame articlesDf:
1.+------+-----------------------------+-------------------+
2.|itemId|attributes |supplier |
3.+------+-----------------------------+-------------------+
4.|1 |[blue, winter, cozy] |Sports Company Inc.|
5.|2 |[red, summer, fresh, cooling]|YetiX |
6.|3 |[green, summer, travel] |Sports Company Inc.|
7.+------+-----------------------------+-------------------+
- A. 1. articlesDf = articlesDf.groupby("col")
2. articlesDf = articlesDf.select(explode(col("attributes")))
3. articlesDf = articlesDf.orderBy("count").select("col")
4. articlesDf = articlesDf.sort("count",ascending=False).select("col")
5. articlesDf = articlesDf.groupby("col").count() - B. 2, 5, 3
- C. 2, 5, 4
- D. 4, 5
- E. 2, 3, 4
- F. 5, 2
Answer: E
Explanation:
Explanation
Correct code block:
articlesDf = articlesDf.select(explode(col('attributes')))
articlesDf = articlesDf.groupby('col').count()
articlesDf = articlesDf.sort('count',ascending=False).select('col')
Output of correct code block:
+-------+
| col|
+-------+
| summer|
| winter|
| blue|
| cozy|
| travel|
| fresh|
| red|
|cooling|
| green|
+-------+
Static notebook | Dynamic notebook: See test 2
NEW QUESTION 27
The code block shown below should write DataFrame transactionsDf as a parquet file to path storeDir, using brotli compression and replacing any previously existing file. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__.format("parquet").__2__(__3__).option(__4__, "brotli").__5__(storeDir)
- A. 1. save
2. mode
3. "replace"
4. "compression"
5. path - B. 1. store
2. with
3. "replacement"
4. "compression"
5. path - C. 1. save
2. mode
3. "ignore"
4. "compression"
5. path - D. 1. write
2. mode
3. "overwrite"
4. "compression"
5. save
(Correct) - E. 1. write
2. mode
3. "overwrite"
4. compression
5. parquet
Answer: A
Explanation:
Explanation
Correct code block:
transactionsDf.write.format("parquet").mode("overwrite").option("compression", "snappy").save(storeDir) Solving this question requires you to know how to access the DataFrameWriter (link below) from the DataFrame API - through DataFrame.write.
Another nuance here is about knowing the different modes available for writing parquet files that determine Spark's behavior when dealing with existing files. These, together with the compression options are explained in the DataFrameWriter.parquet documentation linked below.
Finally, bracket __5__ poses a certain challenge. You need to know which command you can use to pass down the file path to the DataFrameWriter. Both save and parquet are valid options here.
More info:
- DataFrame.write: pyspark.sql.DataFrame.write - PySpark 3.1.1 documentation
- DataFrameWriter.parquet: pyspark.sql.DataFrameWriter.parquet - PySpark 3.1.1 documentation Static notebook | Dynamic notebook: See test 1
NEW QUESTION 28
The code block shown below should return a DataFrame with columns transactionsId, predError, value, and f from DataFrame transactionsDf. Choose the answer that correctly fills the blanks in the code block to accomplish this.
transactionsDf.__1__(__2__)
- A. 1. select
2. "transactionId, predError, value, f" - B. 1. select
2. col(["transactionId", "predError", "value", "f"]) - C. 1. filter
2. "transactionId", "predError", "value", "f" - D. 1. select
2. ["transactionId", "predError", "value", "f"] - E. 1. where
2. col("transactionId"), col("predError"), col("value"), col("f")
Answer: D
Explanation:
Explanation
Correct code block:
transactionsDf.select(["transactionId", "predError", "value", "f"])
The DataFrame.select returns specific columns from the DataFrame and accepts a list as its only argument.
Thus, this is the correct choice here. The option using col(["transactionId", "predError",
"value", "f"]) is invalid, since inside col(), one can only pass a single column name, not a list. Likewise, all columns being specified in a single string like "transactionId, predError, value, f" is not valid syntax.
filter and where filter rows based on conditions, they do not control which columns to return.
Static notebook | Dynamic notebook: See test 2
NEW QUESTION 29
......
