Google Google Certifications PROFESSIONAL-DATA-ENGINEER Questions & Answers

• Question 61:

  Which of these is NOT a way to customize the software on Dataproc cluster instances?

  A. Set initialization actions

  B. Modify configuration files using cluster properties

  C. Configure the cluster using Cloud Deployment Manager

  D. Log into the master node and make changes from there

  Correct Answer: C

  You can access the master node of the cluster by clicking the SSH button next to it in the Cloud Console.

  You can easily use the --properties option of the dataproc command in the Google Cloud SDK to modify many common configuration files when creating a cluster. When creating a Cloud Dataproc cluster, you can specify initialization actions

  in executables and/or scripts that Cloud Dataproc will run on all nodes in your Cloud Dataproc cluster immediately after the cluster is set up. [https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/init-actions] Reference: https://

  cloud.google.com/dataproc/docs/concepts/configuring-clusters/cluster- properties

• Question 62:

  The _________ for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline.

  A. Cloud Dataflow connector

  B. DataFlow SDK

  C. BiqQuery API

  D. BigQuery Data Transfer Service

  Correct Answer: A

  The Cloud Dataflow connector for Cloud Bigtable makes it possible to use Cloud Bigtable in a Cloud Dataflow pipeline. You can use the connector for both batch and streaming operations. Reference: https://cloud.google.com/bigtable/docs/dataflow-hbase

• Question 63:

  Which of these sources can you not load data into BigQuery from?

  A. File upload

  B. Google Drive

  C. Google Cloud Storage

  D. Google Cloud SQL

  Correct Answer: D

  You can load data into BigQuery from a file upload, Google Cloud Storage, Google Drive, or Google Cloud Bigtable. It is not possible to load data into BigQuery directly from Google Cloud SQL. One way to get data from Cloud SQL to BigQuery would be to export data from Cloud SQL to Cloud Storage and then load it from there. Reference: https://cloud.google.com/bigquery/loading-data

• Question 64:

  When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?

  A. 500 TB

  B. 1 GB

  C. 1 TB

  D. 500 GB

  Correct Answer: C

  Cloud Bigtable is not a relational database. It does not support SQL queries, joins, or multi- row transactions. It is not a good solution for less than 1 TB of data.

  Reference:

  https://cloud.google.com/bigtable/docs/overview#title_short_and_other_storage_options

• Question 65:

  To run a TensorFlow training job on your own computer using Cloud Machine Learning Engine, what would your command start with?

  A. gcloud ml-engine local train

  B. gcloud ml-engine jobs submit training

  C. gcloud ml-engine jobs submit training local

  D. You can't run a TensorFlow program on your own computer using Cloud ML Engine .

  Correct Answer: A

  gcloud ml-engine local train - run a Cloud ML Engine training job locally This command runs the specified module in an environment similar to that of a live Cloud ML Engine Training Job. This is especially useful in the case of testing distributed models, as it allows you to validate that you are properly interacting with the Cloud ML Engine cluster configuration. Reference: https://cloud.google.com/sdk/gcloud/reference/ml- engine/local/train

• Question 66:

  Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

  A. Hive

  B. Pig

  C. YARN

  D. Spark

  Correct Answer: ABD

  Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.

  Reference:

  https://cloud.google.com/dataproc/docs/resources/faq#what_type_of_jobs_can_i_run

• Question 67:

  Which of the following statements about the Wide and Deep Learning model are true? (Select 2 answers.)

  A. The wide model is used for memorization, while the deep model is used for generalization.

  B. A good use for the wide and deep model is a recommender system.

  C. The wide model is used for generalization, while the deep model is used for memorization.

  D. A good use for the wide and deep model is a small-scale linear regression problem.

  Correct Answer: AB

  Can we teach computers to learn like humans do, by combining the power of memorization and generalization? It's not an easy question to answer, but by jointly training a wide linear model (for memorization) alongside a deep neural network (for generalization), one can combine the strengths of both to bring us one step closer. At Google, we call it Wide and Deep Learning. It's useful for generic large-scale regression and classification problems with sparse inputs (categorical features with a large number of possible feature values), such as recommender systems, search, and ranking problems. Reference: https://research.googleblog.com/2016/06/wide-deep-learning-better-together- with.html

• Question 68:

  Which of the following are feature engineering techniques? (Select 2 answers)

  A. Hidden feature layers

  B. Feature prioritization

  C. Crossed feature columns

  D. Bucketization of a continuous feature

  Correct Answer: CD

  Selecting and crafting the right set of feature columns is key to learning an effective model. Bucketization is a process of dividing the entire range of a continuous feature into a set of consecutive bins/buckets, and then converting the original numerical feature into a bucket ID (as a categorical feature) depending on which bucket that value falls into. Using each base feature column separately may not be enough to explain the data. To learn the differences between different feature combinations, we can add crossed feature columns to the model.

  Reference: https://www.tensorflow.org/tutorials/wide#selecting_and_engineering_features_for_the_mo del

• Question 69:

  What are all of the BigQuery operations that Google charges for?

  A. Storage, queries, and streaming inserts

  B. Storage, queries, and loading data from a file

  C. Storage, queries, and exporting data

  D. Queries and streaming inserts

  Correct Answer: A

  Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations. Reference: https://cloud.google.com/bigquery/pricing

• Question 70:

  The Dataflow SDKs have been recently transitioned into which Apache service?

  A. Apache Spark

  B. Apache Hadoop

  C. Apache Kafka

  D. Apache Beam

  Correct Answer: D

  Dataflow SDKs are being transitioned to Apache Beam, as per the latest Google directive Reference: https://cloud.google.com/dataflow/docs/