[Jan-2022] The Best Google Cloud Certified Study Guide for the Professional-Data-Engineer Exam
Professional-Data-Engineer certification guide Q&A from Training Expert ActualVCE
Google Professional-Data-Engineer Exam Syllabus Topics:
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NEW QUESTION 81
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?
- A. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
- B. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
- C. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
- D. Add capacity (memory and disk space) to the database server by the order of 200.
Answer: A
Explanation:
It provides the least amount of inconvenience over using pre-specified date ranges or one table per clinic while also increasing performance due to avoiding self-joins.
NEW QUESTION 82
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?
- A. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
- B. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
- C. Add capacity (memory and disk space) to the database server by the order of 200.
- D. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
Answer: A
NEW QUESTION 83
You have data pipelines running on BigQuery, Cloud Dataflow, and Cloud Dataproc. You need to perform health checks and monitor their behavior, and then notify the team managing the pipelines if they fail. You also need to be able to work across multiple projects. Your preference is to use managed products of features of the platform. What should you do?
- A. Run a Virtual Machine in Compute Engine with Airflow, and export the information to Stackdriver
- B. Export the information to Cloud Stackdriver, and set up an Alerting policy
- C. Export the logs to BigQuery, and set up App Engine to read that information and send emails if you find a failure in the logs
- D. Develop an App Engine application to consume logs using GCP API calls, and send emails if you find a failure in the logs
Answer: A
Explanation:
Explanation/Reference:
NEW QUESTION 84
Cloud Dataproc is a managed Apache Hadoop and Apache _____ service.
- A. Fire
- B. Spark
- C. Ignite
- D. Blaze
Answer: B
Explanation:
Explanation
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
Reference: https://cloud.google.com/dataproc/docs/
NEW QUESTION 85
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster
____.
- A. master node
- B. worker node
- C. application node
- D. conditional node
Answer: A
Explanation:
Explanation
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster master node. The cluster master-host-name is the name of your Cloud Dataproc cluster followed by an -m suffix-for example, if your cluster is named "my-cluster", the master-host-name would be "my-cluster-m".
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces
NEW QUESTION 86
Which of these is not a supported method of putting data into a partitioned table?
- A. 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 "$YYYYMMDD".
- B. Create a partitioned table and stream new records to it every day.
- C. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.
- D. Use ORDER BY to put a table's rows into chronological order and then change the table's type to
"Partitioned".
Answer: D
Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name.
Reference: https://cloud.google.com/bigquery/docs/partitioned-tables
NEW QUESTION 87
What are two of the benefits of using denormalized data structures in BigQuery?
- A. Reduces the amount of storage required, increases query speed
- B. Reduces the amount of data processed, reduces the amount of storage required
- C. Reduces the amount of data processed, increases query speed
- D. Increases query speed, makes queries simpler
Answer: D
Explanation:
Denormalization increases query speed for tables with billions of rows because BigQuery's performance degrades when doing JOINs on large tables, but with a denormalized data structure, you don't have to use JOINs, since all of the data has been combined into one table. Denormalization also makes queries simpler because you do not have to use JOIN clauses.
Denormalization increases the amount of data processed and the amount of storage required because it creates redundant data.
Reference:
https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data
NEW QUESTION 88
You are running a pipeline in Cloud Dataflow that receives messages from a Cloud Pub/Sub topic and writes the results to a BigQuery dataset in the EU. Currently, your pipeline is located in europe-west4 and has a maximum of 3 workers, instance type n1-standard-1. You notice that during peak periods, your pipeline is struggling to process records in a timely fashion, when all 3 workers are at maximum CPU utilization. Which two actions can you take to increase performance of your pipeline? (Choose two.)
- A. Create a temporary table in Cloud Bigtable that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Bigtable to BigQuery
- B. Change the zone of your Cloud Dataflow pipeline to run in us-central1
- C. Create a temporary table in Cloud Spanner that will act as a buffer for new data. Create a new step in your pipeline to write to this table first, and then create a new pipeline to write from Cloud Spanner to BigQuery
- D. Increase the number of max workers
- E. Use a larger instance type for your Cloud Dataflow workers
Answer: C,E
NEW QUESTION 89
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?
- A. Use Cloud Dataproc to run your transformations. Use the diagnose command to generate an operational output archive. Locate the bottleneck and adjust cluster resources.
- B. Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs.
Configure the job to use non-default Compute Engine machine types when needed. - C. Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.
- D. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.
Answer: D
Explanation:
Dataflow is good with autoscaling and stackdriver to monitor CPU and Storage.
NEW QUESTION 90
You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?
- A. Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
- B. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
- C. Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.
- D. Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullName. Run a BigQuery load job to load the new CSV file into BigQuery.
Answer: D
Explanation:
Import and Export to Bigquery from Cloud Storage is FREE. Also, when u store the csv files, Cloud Storage is cheaper than Bigquery. For processing Dataproc is cheaper than Dataflow.
NEW QUESTION 91
You have Google Cloud Dataflow streaming pipeline running with a Google Cloud Pub/Sub subscription as the source. You need to make an update to the code that will make the new Cloud Dataflow pipeline incompatible with the current version. You do not want to lose any data when making this update. What should you do?
- A. Create a new pipeline that has a new Cloud Pub/Sub subscription and cancel the old pipeline.
- B. Update the current pipeline and provide the transform mapping JSON object.
- C. Create a new pipeline that has the same Cloud Pub/Sub subscription and cancel the old pipeline.
- D. Update the current pipeline and use the drain flag.
Answer: B
Explanation:
If any transform names in your pipeline have changed, you must supply a transform mapping and pass it using the --transformNameMapping option.
https://cloud.google.com/dataflow/docs/guides/updating-a-pipeline#preventing_compatibility_breaks
NEW QUESTION 92
As your organization expands its usage of GCP, many teams have started to create their own projects.
Projects are further multiplied to accommodate different stages of deployments and target audiences. Each project requires unique access control configurations. The central IT team needs to have access to all projects.
Furthermore, data from Cloud Storage buckets and BigQuery datasets must be shared for use in other projects in an ad hoc way. You want to simplify access control management by minimizing the number of policies.
Which two steps should you take? (Choose two.)
- A. Use Cloud Deployment Manager to automate access provision.
- B. For each Cloud Storage bucket or BigQuery dataset, decide which projects need access. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
- C. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
- D. Introduce resource hierarchy to leverage access control policy inheritance.
- E. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
Answer: A,E
Explanation:
Explanation
NEW QUESTION 93
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?
- A. Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.
- B. Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.
- C. Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.
- D. Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.
Answer: D
NEW QUESTION 94
You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time. Which two methods can accomplish this? (Choose two.)
- A. Use managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
- B. Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.
- C. Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.
- D. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
- E. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
Answer: C,D
NEW QUESTION 95
You have Cloud Functions written in Node.js that pull messages from Cloud Pub/Sub and send the data to BigQuery. You observe that the message processing rate on the Pub/Sub topic is orders of magnitude higher than anticipated, but there is no error logged in Stackdriver Log Viewer. What are the two most likely causes of this problem? (Choose two.)
- A. The subscriber code does not acknowledge the messages that it pulls.
- B. Total outstanding messages exceed the 10-MB maximum.
- C. The subscriber code cannot keep up with the messages.
- D. Publisher throughput quota is too small.
- E. Error handling in the subscriber code is not handling run-time errors properly.
Answer: C,E
NEW QUESTION 96
You are designing a data processing pipeline. The pipeline must be able to scale automatically as load increases. Messages must be processed at least once and must be ordered within windows of 1 hour. How should you design the solution?
- A. Use Cloud Pub/Sub for message ingestion and Cloud Dataflow for streaming analysis.
- B. Use Apache Kafka for message ingestion and use Cloud Dataproc for streaming analysis.
- C. Use Apache Kafka for message ingestion and use Cloud Dataflow for streaming analysis.
- D. Use Cloud Pub/Sub for message ingestion and Cloud Dataproc for streaming analysis.
Answer: D
Explanation:
Explanation
NEW QUESTION 97
Your company built a TensorFlow neural-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly.
What method can you employ to address this?
- A. Dropout Methods
- B. Dimensionality Reduction
- C. Serialization
- D. Threading
Answer: A
Explanation:
https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877
NEW QUESTION 98
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 preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.
- B. Increase the cluster size with more non-preemptible workers.
- C. Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.
- D. Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.
Answer: D
Explanation:
Explanation
Reference https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/flex
NEW QUESTION 99
What is the HBase Shell for Cloud Bigtable?
- A. The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables.
- B. The HBase shell is a GUI based interface that performs administrative tasks, such as creating and deleting tables.
- C. The HBase shell is a command-line tool that performs only user account management functions to grant access to Cloud Bigtable instances.
- D. The HBase shell is a hypervisor based shell that performs administrative tasks, such as creating and deleting new virtualized instances.
Answer: A
Explanation:
The HBase shell is a command-line tool that performs administrative tasks, such as creating and deleting tables. The Cloud Bigtable HBase client for Java makes it possible to use the HBase shell to connect to Cloud Bigtable.
Reference: https://cloud.google.com/bigtable/docs/installing-hbase-shell
NEW QUESTION 100
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