Google Google Certifications PROFESSIONAL-DATA-ENGINEER Questions & Answers

• Question 71:

  Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

  A. Use K-means Clustering to detect faces in the pixels.

  B. Use feature engineering to add features for eyes, noses, and mouths to the input data.

  C. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.

  D. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

  Correct Answer: C

  Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as "deep" learning. So deep is a strictly defined, technical term that means more than one hidden layer. In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer's output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the previous layer. A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines. Feature engineering is difficult to perform on raw image data. K-means Clustering is an unsupervised learning method used to categorize unlabeled data. Reference: https://deeplearning4j.org/neuralnet-overview

• Question 72:

  Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

  A. BETWEEN

  B. WHERE

  C. SELECT

  D. LIMIT

  Correct Answer: C

  SELECT allows you to query specific columns rather than the whole table. LIMIT, BETWEEN, and WHERE clauses will not reduce the number of columns processed by BigQuery. Reference: https://cloud.google.com/bigquery/launch- checklist#architecture_design_and_development_checklist

• Question 73:

  Which of the following is NOT true about Dataflow pipelines?

  A. Dataflow pipelines are tied to Dataflow, and cannot be run on any other runner

  B. Dataflow pipelines can consume data from other Google Cloud services

  C. Dataflow pipelines can be programmed in Java

  D. Dataflow pipelines use a unified programming model, so can work both with streaming and batch data sources

  Correct Answer: A

  Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/

• Question 74:

  Which of the following statements about Legacy SQL and Standard SQL is not true?

  A. Standard SQL is the preferred query language for BigQuery.

  B. If you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.

  C. One difference between the two query languages is how you specify fully-qualified table names (i.e. table names that include their associated project name).

  D. You need to set a query language for each dataset and the default is Standard SQL.

  Correct Answer: D

  You do not set a query language for each dataset. It is set each time you run a query and the default query language is Legacy SQL. Standard SQL has been the preferred query language since BigQuery 2.0 was released. In legacy SQL, to query a table with a project-qualified name, you use a colon, :, as a separator. In standard SQL, you use a period, ., instead. Due to the differences in syntax between the two query languages (such as with project- qualified table names), if you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL. Reference: https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql

• Question 75:

  You want to build a managed Hadoop system as your data lake. The data transformation process is composed of a series of Hadoop jobs executed in sequence. To accomplish the design of separating storage from compute, you decided to

  use the Cloud Storage connector to store all input data, output data, and intermediary data. However, you noticed that one Hadoop job runs very slowly with Cloud Dataproc, when compared with the on- premises bare-metal Hadoop

  environment (8-core nodes with 100-GB RAM). Analysis shows that this particular Hadoop job is disk I/O intensive. You want to resolve the issue.

  What should you do?

  A. Allocate sufficient memory to the Hadoop cluster, so that the intermediary data of that particular Hadoop job can be held in memory

  B. Allocate sufficient persistent disk space to the Hadoop cluster, and store the intermediate data of that particular Hadoop job on native HDFS

  C. Allocate more CPU cores of the virtual machine instances of the Hadoop cluster so that the networking bandwidth for each instance can scale up

  D. Allocate additional network interface card (NIC), and configure link aggregation in the operating system to use the combined throughput when working with Cloud Storage

  Correct Answer: A

• Question 76:

  Which software libraries are supported by Cloud Machine Learning Engine?

  A. Theano and TensorFlow

  B. Theano and Torch

  C. TensorFlow

  D. TensorFlow and Torch

  Correct Answer: C

  Cloud ML Engine mainly does two things:

  Enables you to train machine learning models at scale by running TensorFlow training applications in the cloud.

  Hosts those trained models for you in the cloud so that you can use them to get predictions about new data.

  Reference: https://cloud.google.com/ml-engine/docs/technical-overview#what_it_does

• Question 77:

  Your chemical company needs to manually check documentation for customer order. You use a pull subscription in Pub/Sub so that sales agents get details from the order. You must ensure that you do not process orders twice with different sales agents and that you do not add more complexity to this workflow. What should you do?

  A. Create a transactional database that monitors the pending messages.

  B. Create a new Pub/Sub push subscription to monitor the orders processed in the agent's system.

  C. Use Pub/Sub exactly-once delivery in your pull subscription.

  D. Use a Deduphcate PTransform in Dataflow before sending the messages to the sales agents.

  Correct Answer: C

  Pub/Sub exactly-once delivery is a feature that guarantees that subscriptions do not receive duplicate deliveries of messages based on a Pub/Sub-defined unique message ID. This feature is only supported by the pull subscription type, which

  is what you are using in this scenario. By enabling exactly-once delivery, you can ensure that each order is processed only once by a sales agent, and that no order is lost or duplicated. This also simplifies your workflow, as you do not need to

  create a separate database or subscription to monitor the pending or processed messages.

  References: Exactly-once delivery | Cloud Pub/Sub Documentation Cloud Pub/Sub Exactly-once Delivery feature is now Generally Available (GA)

• Question 78:

  Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert.

  What is the most likely cause of this problem?

  A. They have not assigned the timestamp, which causes the job to fail

  B. They have not set the triggers to accommodate the data coming in late, which causes the job to fail

  C. They have not applied a global windowing function, which causes the job to fail when the pipeline is created

  D. They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created

  Correct Answer: C

• Question 79:

  You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:

  1.

  Executing the transformations on a schedule

  2.

  Enabling non-developer analysts to modify transformations Providing a graphical tool for designing transformations

  What should you do?

  A. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

  B. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables together with a SQL query

  C. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data's schema changes

  D. Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe. Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery

  Correct Answer: A

  you can use dataprep for continuously changing target schema In general, a target consists of the set of information required to define the expected data in a dataset. Often referred to as a "schema," this target schema information can include:

  Names of columns Order of columns Column data types Data type format Example rows of data A dataset associated with a target is expected to conform to the requirements of the schema. Where there are differences between target schema and dataset schema, a validation indicator (or schema tag) is displayed.

  https:// cloud.google.com/dataprep/docs/html/Overview-of-RapidTarget_136155049

• Question 80:

  You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

  A. Increase the cluster size with more non-preemptible workers.

  B. Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.

  C. Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.

  D. Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.

  Correct Answer: D

  Reference https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/flex