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NEW QUESTION # 28
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a proxy.
- A. VPN
- B. SOCKS
- C. HTTPS
- D. HTTP
Answer: B
Explanation:
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
NEW QUESTION # 29
Which of the following is NOT one of the three main types of triggers that Dataflow supports?
- A. Trigger based on time
- B. Trigger based on element count
- C. Trigger that is a combination of other triggers
- D. Trigger based on element size in bytes
Answer: D
Explanation:
There are three major kinds of triggers that Dataflow supports: 1. Time-based triggers 2. Data-driven triggers. You can set a trigger to emit results from a window when that window has received a certain number of data elements. 3. Composite triggers. These triggers combine multiple time-based or data-driven triggers in some logical way Reference: https://cloud.google.com/dataflow/model/triggers
NEW QUESTION # 30
You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name Passing Certification Exams Made Easy visit - https://www.2PassEasy.com from the data source and then write to an output PCollection.
Tom,555 X street Tim,553 Y street Sam, 111 Z street
Which operation is best suited for the above data processing requirement?
- A. Source API
- B. Sink API
- C. ParDo
- D. Data extraction
Answer: C
Explanation:
In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection.
Reference: https://cloud.google.com/dataflow/model/par-do
NEW QUESTION # 31
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?
- A. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtabl
- B. If that number falls below 4000, send an alert.
- C. Set a sliding time window of 1 hour every 5 minute
- D. If that number falls below 4000, send an alert.
- E. Set a fixed time window of 1 hour.Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
- F. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuer
- G. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour
- H. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
- I. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Su
- J. Consume the stream of data in Cloud Dataflow using Kafka I
- K. Consume the stream of data in Cloud Dataflow using Kafka I
- L. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour
- M. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
Answer: M
NEW QUESTION # 32
You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?
- A. Cloud Spanner
- B. Cloud Bigtable
- C. Cloud Datastore
- D. Cloud SQL
Answer: D
NEW QUESTION # 33
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?
- A. OutputCriteria
- B. Triggers
- C. Sessions
- D. Windows
Answer: B
Explanation:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/transforms/windowing/Tri
NEW QUESTION # 34
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:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?
- A. 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
- B. Load each month's CSV data into BigQuery, and write a SQL query to transform the data to a standard scheme
- C. Merge the transformed tables together with a SQL query
- D. Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformatio
- E. The Python code should be stored in a revision control system and modified as the incoming data's schema changes
- F. Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis
Answer: D
NEW QUESTION # 35
Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?
- A. An hourly watermark
- B. An event time trigger
- C. A processing time trigger
- D. The with Allowed Lateness method
Answer: C
Explanation:
When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window.
Processing time triggers. These triggers operate on the processing time - the time when the data element is processed at any given stage in the pipeline. Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based.
Reference: https://beam.apache.org/documentation/programming-guide/#triggers
NEW QUESTION # 36
MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds). Which combination of Google Cloud Platform products should you recommend?
- A. BigQuery and Cloud Storage
- B. Cloud Datastore and Cloud Bigtable
- C. BigQuery and Cloud Bigtable
- D. Cloud Bigtable and Cloud SQL
Answer: C
NEW QUESTION # 37
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?
- A. Create an API using App Engine to receive and send messages to the applications
- B. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
- C. Create a table on Cloud SQL, and insert and delete rows with the job information
- D. Create a table on Cloud Spanner, and insert and delete rows with the job information
Answer: B
NEW QUESTION # 38
What are the minimum permissions needed for a service account used with Google Dataproc?
- A. Read and write to Google Cloud Storage; write to Google Cloud Logging
- B. Execute to Google Cloud Storage; execute to Google Cloud Logging
- C. Write to Google Cloud Storage; read to Google Cloud Logging
- D. Execute to Google Cloud Storage; write to Google Cloud Logging
Answer: A
Explanation:
Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes
NEW QUESTION # 39
MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?
- A. Rowkey: device_idColumn data: date, data_point
- B. Rowkey: dateColumn data: device_id, data_point
- C. Rowkey: date#device_idColumn data: data_point
- D. Rowkey: date#data_pointColumn data: device_id
- E. Rowkey: data_pointColumn data: device_id, date
Answer: E
NEW QUESTION # 40
You work for a global shipping company. You want to train a model on 40 TB of data to predict which ships in each geographic region are likely to cause delivery delays on any given day. The model will be based on multiple attributes collected from multiple sources. Telemetry data, including location in GeoJSON format, will be pulled from each ship and loaded every hour. You want to have a dashboard that shows how many and which ships are likely to cause delays within a region. You want to use a storage solution that has native functionality for prediction and geospatial processing. Which storage solution should you use?
- A. Cloud Bigtable
- B. Cloud SQL for PostgreSQL
- C. Cloud Datastore
- D. BigQuery
Answer: D
NEW QUESTION # 41
Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?
- A. The CSV data loaded in BigQuery is not using BigQuery's default encoding.
- B. The CSV data has invalid rows that were skipped on import.
- C. The CSV data has not gone through an ETL phase before loading into BigQuery.
- D. The CSV data loaded in BigQuery is not flagged as CSV.
Answer: B
NEW QUESTION # 42
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 2 answers.
- A. Create distinct groups for various teams, and specify groups in Cloud IAM policies.
- B. Use Cloud Deployment Manager to automate access provision.
- C. Introduce resource hierarchy to leverage access control policy inheritance.
- D. Find all the active members who have access to these projects, and create a Cloud IAM policy to grant access to all these users.
- E. For each Cloud Storage bucket or BigQuery dataset, decide which projects need acces
- F. Only use service accounts when sharing data for Cloud Storage buckets and BigQuery datasets.
Answer: A,C
NEW QUESTION # 43
Cloud Dataproc is a managed Apache Hadoop and Apache service.
- A. Spark
- B. Ignite
- C. Fire
- D. Blaze
Answer: A
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 # 44
Flowlogistic's management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?
- A. Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage
- B. Cloud Pub/Sub, Cloud SQL, and Cloud Storage
- C. Cloud Load Balancing, Cloud Dataflow, and Cloud Storage
- D. Cloud Pub/Sub, Cloud Dataflow, and Local SSD
Answer: B
NEW QUESTION # 45
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?
- A. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
- B. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
- C. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
- D. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.
Answer: B
Explanation:
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance, If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance
NEW QUESTION # 46
You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?
- A. Create a scheduled query to make copies of the data to tables suffixed with the time of the backu
- B. In the event of an emergency, use a point-in-time snapshot to recover the data.
- C. In the event of an emergency, use the backup copy of the table.
- D. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup
- E. Set the BigQuery dataset to be multi-regional
- F. Set the BigQuery dataset to be regiona
- G. Set the BigQuery dataset to be regiona
- H. Set the BigQuery dataset to be multi-regional
- I. In the event of an emergency, use the backup copy of the table.
- J. In the event of an emergency, use a point-in-time snapshot to recover the data.
Answer: B
NEW QUESTION # 47
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 transformation
- B. Use the diagnose command to generate an operational output archiv
- C. Use Cloud Dataproc to run your transformation
- D. Use Cloud Dataflow to run your transformation
- E. Monitor the total execution time for a sampling of job
- F. Configure the job to use non-default Compute Engine machine types when needed.
- G. Use the default autoscaling setting for worker instances.
- H. Monitor the job system lag with Stackdrive
- I. Monitor CPU utilization for the cluste
- J. Resize the number of worker nodes in your cluster via the command line.
- K. Locate the bottleneck and adjust cluster resources.
- L. Use Cloud Dataflow to run your transformation
Answer: I
NEW QUESTION # 48
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. Create a partitioned table and stream new records to it every day.
- C. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
- D. 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 "$YYYYMMDD".
Answer: C
Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name. Reference: https://cloud.google.com/bigquery/docs/partitioned-tables
NEW QUESTION # 49
You want to migrate an on-premises Hadoop system to Cloud Dataproc. Hive is the primary tool in use, and the data format is Optimized Row Columnar (ORC). All ORC files have been successfully copied to a Cloud Storage bucket. You need to replicate some data to the cluster's local Hadoop Distributed File System (HDFS) to maximize performance. What are two ways to start using Hive in Cloud Dataproc? (Choose two.)
- A. Replicate external Hive tables to the native ones.
- B. Mount the Hive tables from HDFS.
- C. Leverage BigQuery connector for Hadoop to mount the BigQuery tables as external Hive table
- D. Mount the Hive tables locally.
- E. Then run the Hadoop utility to copy them do HDF
- F. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to HDF
- G. Load the ORC files into BigQuer
- H. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to any node of the Dataproc cluste
- I. Replicate external Hive tables to the native ones.
- J. Leverage Cloud Storage connector for Hadoop to mount the ORC files as external Hive table
- K. Mount the Hive tables locally.
- L. Run the gsutil utility to transfer all ORC files from the Cloud Storage bucket to the master node of the Dataproc cluste
Answer: D,H
NEW QUESTION # 50
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