Google Google Certifications PROFESSIONAL-DATA-ENGINEER Questions & Answers

• Question 1:

  Which of these are examples of a value in a sparse vector? (Select 2 answers.)

  A. [0, 5, 0, 0, 0, 0]

  B. [0, 0, 0, 1, 0, 0, 1]

  C. [0, 1]

  D. [1, 0, 0, 0, 0, 0, 0]

  Correct Answer: CD

  Categorical features in linear models are typically translated into a sparse vector in which each possible value has a corresponding index or id. For example, if there are only three possible eye colors you can represent 'eye_color' as a length

  3 vector: 'brown' would become [1, 0, 0], 'blue' would become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called "sparse" because they may be very long, with many zeros, when the set of possible values is very large (such

  as all English words). [0, 0, 0, 1, 0, 0, 1] is not a sparse vector because it has two 1s in it. A sparse vector contains only a single 1.

  [0, 5, 0, 0, 0, 0] is not a sparse vector because it has a 5 in it. Sparse vectors only contain 0s and 1s.

  Reference:

  https://www.tensorflow.org/tutorials/linear#feature_columns_and_transformations

• Question 2:

  Which Google Cloud Platform service is an alternative to Hadoop with Hive?

  A. Cloud Dataflow

  B. Cloud Bigtable

  C. BigQuery

  D. Cloud Datastore

  Correct Answer: C

  Apache Hive is a data warehouse software project built on top of Apache Hadoop for providing data summarization, query, and analysis. Google BigQuery is an enterprise data warehouse. Reference: https://en.wikipedia.org/wiki/Apache_Hive

• Question 3:

  Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.

  SELECT person FROM `project1.example.table1` WHERE city = "London"

  How would you correct the error?

  A. Add ", UNNEST(person)" before the WHERE clause.

  B. Change "person" to "person.city".

  C. Change "person" to "city.person".

  D. Add ", UNNEST(city)" before the WHERE clause.

  Correct Answer: A

  To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma. Reference: https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_results

• Question 4:

  Cloud Dataproc charges you only for what you really use with _____ billing.

  A. month-by-month

  B. minute-by-minute

  C. week-by-week

  D. hour-by-hour

  Correct Answer: B

  One of the advantages of Cloud Dataproc is its low cost. Dataproc charges for what you really use with minute-by-minute billing and a low, ten-minute-minimum billing period. Reference: https://cloud.google.com/dataproc/docs/concepts/ overview

• Question 5:

  Cloud Bigtable is a recommended option for storing very large amounts of ____________________________?

  A. multi-keyed data with very high latency

  B. multi-keyed data with very low latency

  C. single-keyed data with very low latency

  D. single-keyed data with very high latency

  Correct Answer: C

  Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key.

  Cloud Bigtable is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at low latency, and it is an ideal data source for MapReduce operations.

  Reference: https://cloud.google.com/bigtable/docs/overview

• Question 6:

  When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a ____ proxy.

  A. HTTPS

  B. VPN

  C. SOCKS

  D. HTTP

  Correct Answer: C

  When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel. Reference: https://cloud.google.com/dataproc/docs/ concepts/cluster-web- interfaces#interfaces

• Question 7:

  When a Cloud Bigtable node fails, ____ is lost.

  A. all data

  B. no data

  C. the last transaction

  D. the time dimension

  Correct Answer: B

  A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node. Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result: Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node. Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node. When a Cloud Bigtable node fails, no data is lost Reference: https://cloud.google.com/bigtable/docs/overview

• Question 8:

  What are two methods that can be used to denormalize tables in BigQuery?

  A. 1) Split table into multiple tables; 2) Use a partitioned table

  B. 1) Join tables into one table; 2) Use nested repeated fields

  C. 1) Use a partitioned table; 2) Join tables into one table

  D. 1) Use nested repeated fields; 2) Use a partitioned table

  Correct Answer: B

  The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record,

  along with the accompanying dimensions such as order and customer information. The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data.

  Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information,

  and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.

  Reference: https://cloud.google.com/solutions/bigquery-data- warehouse#denormalizing_data

• Question 9:

  Which of these is not a supported method of putting data into a partitioned table?

  A. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.

  B. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".

  C. Create a partitioned table and stream new records to it every day.

  D. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".

  Correct Answer: D

  You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name. Reference: https://cloud.google.com/bigquery/docs/partitioned-tables

• Question 10:

  Which methods can be used to reduce the number of rows processed by BigQuery?

  A. Splitting tables into multiple tables; putting data in partitions

  B. Splitting tables into multiple tables; putting data in partitions; using the LIMIT clause

  C. Putting data in partitions; using the LIMIT clause

  D. Splitting tables into multiple tables; using the LIMIT clause

  Correct Answer: A

  If you split a table into multiple tables (such as one table for each day), then you can limit your query to the data in specific tables (such as for particular days). A better method is to use a partitioned table, as long as your data can be separated by the day. If you use the LIMIT clause, BigQuery will still process the entire table. Reference: https://cloud.google.com/bigquery/docs/partitioned-tables