by Databricks Associate-Developer-Apache-Spark Reliable Dumps Ppt What’s more, the excellent dumps can stand the test rather than just talk about it, Please, submit your Exam Score Report in PDF format within 7 (seven) days of your exam date to [email protected]VCE4Dumps Associate-Developer-Apache-Spark Exam Actual Tests.com, We continuously update our products by adding latest questions in our Associate-Developer-Apache-Spark PDF files, You can identify and overcome your shortcomings, which will eventually make you an expert in solving the Databricks Associate-Developer-Apache-Spark exam problems.
We hereby guarantee if you fail exam we will Associate-Developer-Apache-Spark Exam Material refund the test dumps cost to you soon, Permanent Storage/Nonvolatile Memory, The actors need to redo their parts, lighting and props Reliable Associate-Developer-Apache-Spark Dumps Ppt need to be adjusted, and background elements such as cars and people need to be reset.
Download Associate-Developer-Apache-Spark Exam Dumps >> https://www.vce4dumps.com/Associate-Developer-Apache-Spark-valid-torrent.html
Perhaps you begin dreaming about all the money you Associate-Developer-Apache-Spark Reliable Real Exam could make if you knew the secrets to predicting stock prices, If you are nervous on your Associate-Developer-Apache-Spark exam for you always have the problem on the Associate-Developer-Apache-Spark Latest Materials time-schedule or feeling lack of confidence on the condition that you go to the real exam room.
What’s more, the excellent dumps can stand the test rather than just https://www.vce4dumps.com/Associate-Developer-Apache-Spark-valid-torrent.html talk about it, Please, submit your Exam Score Report in PDF format within 7 (seven) days of your exam date to [email protected]VCE4Dumps.com.
Free PDF Quiz 2023 Databricks Fantastic Associate-Developer-Apache-Spark: Databricks Certified Associate Developer for Apache Spark 3.0 Exam Reliable Dumps Ppt
We continuously update our products by adding latest questions in our Associate-Developer-Apache-Spark PDF files, You can identify and overcome your shortcomings, which will eventually make you an expert in solving the Databricks Associate-Developer-Apache-Spark exam problems.
Such a valuable acquisition priced reasonably of our Associate-Developer-Apache-Spark study guide is offered before your eyes, you can feel assured to take good advantage of, Refuse dull pure theory, Associate-Developer-Apache-Spark pass-king torrent provides you study manners as many as possible.
All the necessary information about our complete range of Associate-Developer-Apache-Spark certification tests is given below, At first you can free download part of exercises questions and answers about Associate-Developer-Apache-Spark valid exam pdf as a try, so that you can check the reliability of our product.
It saves you a lot of time to study several hard books, only our questions and answers of Associate-Developer-Apache-Spark pass for sure materials can be more functional than too many invalid books.
All you need to do is contact the Customer Support and request for the exam you Exam Associate-Developer-Apache-Spark Actual Tests like, We understand the value of your time and money, which is why every question and answer on DumpsArchive has been verified by Databricks experts.
2023 Valid Associate-Developer-Apache-Spark – 100% Free Reliable Dumps Ppt | Databricks Certified Associate Developer for Apache Spark 3.0 Exam Exam Actual Tests
How to pass the Associate-Developer-Apache-Spark exam easily?
Download Databricks Certified Associate Developer for Apache Spark 3.0 Exam Exam Dumps >> https://www.vce4dumps.com/Associate-Developer-Apache-Spark-valid-torrent.html
NEW QUESTION 36
Which of the following code blocks returns a 2-column DataFrame that shows the distinct values in column productId and the number of rows with that productId in DataFrame transactionsDf?
- A. transactionsDf.groupBy(“productId”).select(count(“value”))
- B. transactionsDf.count(“productId”).distinct()
- C. transactionsDf.groupBy(“productId”).agg(col(“value”).count())
- D. transactionsDf.groupBy(“productId”).count()
- E. transactionsDf.count(“productId”)
Answer: D
Explanation:
Explanation
transactionsDf.groupBy(“productId”).count()
Correct. This code block first groups DataFrame transactionsDf by column productId and then counts the rows in each group.
transactionsDf.groupBy(“productId”).select(count(“value”))
Incorrect. You cannot call select on a GroupedData object (the output of a groupBy) statement.
transactionsDf.count(“productId”)
No. DataFrame.count() does not take any arguments.
transactionsDf.count(“productId”).distinct()
Wrong. Since DataFrame.count() does not take any arguments, this option cannot be right.
transactionsDf.groupBy(“productId”).agg(col(“value”).count())
False. A Column object, as returned by col(“value”), does not have a count() method. You can see all available methods for Column object linked in the Spark documentation below.
More info: pyspark.sql.DataFrame.count – PySpark 3.1.2 documentation, pyspark.sql.Column – PySpark
3.1.2 documentation
Static notebook | Dynamic notebook: See test 3
NEW QUESTION 37
Which of the following statements about lazy evaluation is incorrect?
- A. Execution is triggered by transformations.
- B. Predicate pushdown is a feature resulting from lazy evaluation.
- C. Spark will fail a job only during execution, but not during definition.
- D. Lineages allow Spark to coalesce transformations into stages
- E. Accumulators do not change the lazy evaluation model of Spark.
Answer: A
Explanation:
Explanation
Execution is triggered by transformations.
Correct. Execution is triggered by actions only, not by transformations.
Lineages allow Spark to coalesce transformations into stages.
Incorrect. In Spark, lineage means a recording of transformations. This lineage enables lazy evaluation in Spark.
Predicate pushdown is a feature resulting from lazy evaluation.
Wrong. Predicate pushdown means that, for example, Spark will execute filters as early in the process as possible so that it deals with the least possible amount of data in subsequent transformations, resulting in a performance improvements.
Accumulators do not change the lazy evaluation model of Spark.
Incorrect. In Spark, accumulators are only updated when the query that refers to the is actually executed. In other words, they are not updated if the query is not (yet) executed due to lazy evaluation.
Spark will fail a job only during execution, but not during definition.
Wrong. During definition, due to lazy evaluation, the job is not executed and thus certain errors, for example reading from a non-existing file, cannot be caught. To be caught, the job needs to be executed, for example through an action.
NEW QUESTION 38
Which of the following code blocks reads in the parquet file stored at location filePath, given that all columns in the parquet file contain only whole numbers and are stored in the most appropriate format for this kind of data?
- A. 1.spark.read.schema([
2. StructField(“transactionId”, NumberType(), True),
3. StructField(“predError”, IntegerType(), True)
4. ]).load(filePath) - B. 1.spark.read.schema(
2. StructType([
3. StructField(“transactionId”, IntegerType(), True),
4. StructField(“predError”, IntegerType(), True)]
5. )).format(“parquet”).load(filePath) - C. 1.spark.read.schema([
2. StructField(“transactionId”, IntegerType(), True),
3. StructField(“predError”, IntegerType(), True)
4. ]).load(filePath, format=”parquet”) - D. 1.spark.read.schema(
2. StructType([
3. StructField(“transactionId”, StringType(), True),
4. StructField(“predError”, IntegerType(), True)]
5. )).parquet(filePath) - E. 1.spark.read.schema(
2. StructType(
3. StructField(“transactionId”, IntegerType(), True),
4. StructField(“predError”, IntegerType(), True)
5. )).load(filePath)
Answer: B
Explanation:
Explanation
The schema passed into schema should be of type StructType or a string, so all entries in which a list is passed are incorrect.
In addition, since all numbers are whole numbers, the IntegerType() data type is the correct option here.
NumberType() is not a valid data type and StringType() would fail, since the parquet file is stored in the “most appropriate format for this kind of data”, meaning that it is most likely an IntegerType, and Spark does not convert data types if a schema is provided.
Also note that StructType accepts only a single argument (a list of StructFields). So, passing multiple arguments is invalid.
Finally, Spark needs to know which format the file is in. However, all of the options listed are valid here, since Spark assumes parquet as a default when no file format is specifically passed.
More info: pyspark.sql.DataFrameReader.schema – PySpark 3.1.2 documentation and StructType – PySpark 3.1.2 documentation
NEW QUESTION 39
……
Associate-Developer-Apache-Spark Latest Materials >> https://www.vce4dumps.com/Associate-Developer-Apache-Spark-valid-torrent.html
