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NEW QUESTION 1

All Google Cloud Bigtable client requests go through a front-end server they are sent to a Cloud Bigtable node.

  • A. before
  • B. after
  • C. only if
  • D. once

Answer: A

Explanation:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION 2

You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.
Which Google database service should you use?

  • A. Cloud SQL
  • B. BigQuery
  • C. Cloud Bigtable
  • D. Cloud Datastore

Answer: A

NEW QUESTION 3

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. Update the current pipeline and use the drain flag.
  • 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. Create a new pipeline that has a new Cloud Pub/Sub subscription and cancel the old pipeline.

Answer: D

NEW QUESTION 4

Your company is selecting a system to centralize data ingestion and delivery. You are considering messaging and data integration systems to address the requirements. The key requirements are:
Professional-Data-Engineer dumps exhibit The ability to seek to a particular offset in a topic, possibly back to the start of all data ever captured
Professional-Data-Engineer dumps exhibit Support for publish/subscribe semantics on hundreds of topics
Professional-Data-Engineer dumps exhibit Retain per-key ordering Which system should you choose?

  • A. Apache Kafka
  • B. Cloud Storage
  • C. Cloud Pub/Sub
  • D. Firebase Cloud Messaging

Answer: A

NEW QUESTION 5

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.
  • D. table names that include their associated project name).
  • E. You need to set a query language for each dataset and the default is Standard SQL.

Answer: D

Explanation:
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

NEW QUESTION 6

Your infrastructure includes a set of YouTube channels. You have been tasked with creating a process for sending the YouTube channel data to Google Cloud for analysis. You want to design a solution that allows your world-wide marketing teams to perform ANSI SQL and other types of analysis on up-to-date YouTube channels log data. How should you set up the log data transfer into Google Cloud?

  • A. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.
  • B. Use Storage Transfer Service to transfer the offsite backup files to a Cloud Storage Regional bucket as a final destination.
  • C. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Multi-Regional storage bucket as a final destination.
  • D. Use BigQuery Data Transfer Service to transfer the offsite backup files to a Cloud Storage Regional storage bucket as a final destination.

Answer: B

NEW QUESTION 7

You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity ‘Movie’ the property ‘actors’ and the property ‘tags’ have multiple values but the property ‘date released’ does not. A typical query would ask for all movies with actor=<actorname> ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?
Professional-Data-Engineer dumps exhibit

  • A. Option A
  • B. Option B.
  • C. Option C
  • D. Option D

Answer: A

NEW QUESTION 8

MJTelco’s Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?

  • A. The zone
  • B. The number of workers
  • C. The disk size per worker
  • D. The maximum number of workers

Answer: A

NEW QUESTION 9

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. The with Allowed Lateness method
  • D. A processing time trigger

Answer: D

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 10

You need to copy millions of sensitive patient records from a relational database to BigQuery. The total size of the database is 10 TB. You need to design a solution that is secure and time-efficient. What should you do?

  • A. Export the records from the database as an Avro fil
  • B. Upload the file to GCS using gsutil, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
  • C. Export the records from the database as an Avro fil
  • D. Copy the file onto a Transfer Appliance and send itto Google, and then load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.
  • E. Export the records from the database into a CSV fil
  • F. Create a public URL for the CSV file, and then use Storage Transfer Service to move the file to Cloud Storag
  • G. Load the CSV file into BigQuery using the BigQuery web UI in the GCP Console.
  • H. Export the records from the database as an Avro fil
  • I. Create a public URL for the Avro file, and then use Storage Transfer Service to move the file to Cloud Storag
  • J. Load the Avro file into BigQuery using the BigQuery web UI in the GCP Console.

Answer: A

NEW QUESTION 11

Which Java SDK class can you use to run your Dataflow programs locally?

  • A. LocalRunner
  • B. DirectPipelineRunner
  • C. MachineRunner
  • D. LocalPipelineRunner

Answer: B

Explanation:
DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization. Useful for small local execution and tests
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRun

NEW QUESTION 12

Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other’s data. You want to ensure appropriate access to the data. Which three steps should you take? (Choose three.)

  • A. Load data into different partitions.
  • B. Load data into a different dataset for each client.
  • C. Put each client’s BigQuery dataset into a different table.
  • D. Restrict a client’s dataset to approved users.
  • E. Only allow a service account to access the datasets.
  • F. Use the appropriate identity and access management (IAM) roles for each client’s users.

Answer: BDF

NEW QUESTION 13

You are migrating your data warehouse to BigQuery. You have migrated all of your data into tables in a dataset. Multiple users from your organization will be using the data. They should only see certain tables based on their team membership. How should you set user permissions?

  • A. Assign the users/groups data viewer access at the table level for each table
  • B. Create SQL views for each team in the same dataset in which the data resides, and assign the users/groups data viewer access to the SQL views
  • C. Create authorized views for each team in the same dataset in which the data resides, and assign theusers/groups data viewer access to the authorized views
  • D. Create authorized views for each team in datasets created for each tea
  • E. Assign the authorized views data viewer access to the dataset in which the data reside
  • F. Assign the users/groups data viewer access to the datasets in which the authorized views reside

Answer: C

NEW QUESTION 14

Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

  • A. Create a Google Cloud Dataflow job to process the data.
  • B. Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.
  • C. Create a Hadoop cluster on Google Compute Engine that uses persistent disks.
  • D. Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.
  • E. Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.

Answer: A

NEW QUESTION 15

You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

  • A. Eliminate features that are highly correlated to the output labels.
  • B. Combine highly co-dependent features into one representative feature.
  • C. Instead of feeding in each feature individually, average their values in batches of 3.
  • D. Remove the features that have null values for more than 50% of the training records.

Answer: B

NEW QUESTION 16

Your company is streaming real-time sensor data from their factory floor into Bigtable and they have noticed extremely poor performance. How should the row key be redesigned to improve Bigtable performance on queries that populate real-time dashboards?

  • A. Use a row key of the form <timestamp>.
  • B. Use a row key of the form <sensorid>.
  • C. Use a row key of the form <timestamp>#<sensorid>.
  • D. Use a row key of the form >#<sensorid>#<timestamp>.

Answer: A

NEW QUESTION 17

Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?

  • A. Store the common data in BigQuery as partitioned tables.
  • B. Store the common data in BigQuery and expose authorized views.
  • C. Store the common data encoded as Avro in Google Cloud Storage.
  • D. Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.

Answer: B

NEW QUESTION 18

You need to compose visualization for operations teams with the following requirements:
Professional-Data-Engineer dumps exhibit Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)
Professional-Data-Engineer dumps exhibit The report must not be more than 3 hours delayed from live data.
Professional-Data-Engineer dumps exhibit The actionable report should only show suboptimal links.
Professional-Data-Engineer dumps exhibit Most suboptimal links should be sorted to the top.
Professional-Data-Engineer dumps exhibit Suboptimal links can be grouped and filtered by regional geography.
Professional-Data-Engineer dumps exhibit User response time to load the report must be <5 seconds.
You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?

  • A. Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.
  • B. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
  • C. Export the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.
  • D. Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.

Answer: B

NEW QUESTION 19

An online retailer has built their current application on Google App Engine. A new initiative at the company mandates that they extend their application to allow their customers to transact directly via the application.
They need to manage their shopping transactions and analyze combined data from multiple datasets using a business intelligence (BI) tool. They want to use only a single database for this purpose. Which Google Cloud database should they choose?

  • A. BigQuery
  • B. Cloud SQL
  • C. Cloud BigTable
  • D. Cloud Datastore

Answer: C

NEW QUESTION 20

Your company is using WHILECARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
# Syntax error : Expected end of statement but got “-“ at [4:11] SELECT age
FROM
bigquery-public-data.noaa_gsod.gsod WHERE
age != 99
AND_TABLE_SUFFIX = ‘1929’ ORDER BY
age DESC
Which table name will make the SQL statement work correctly?

  • A. ‘bigquery-public-data.noaa_gsod.gsod‘
  • B. bigquery-public-data.noaa_gsod.gsod*
  • C. ‘bigquery-public-data.noaa_gsod.gsod’*
  • D. ‘bigquery-public-data.noaa_gsod.gsod*`

Answer: D

NEW QUESTION 21

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

Answer: D

Explanation:
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

NEW QUESTION 22

If you want to create a machine learning model that predicts the price of a particular stock based on its recent price history, what type of estimator should you use?

  • A. Unsupervised learning
  • B. Regressor
  • C. Classifier
  • D. Clustering estimator

Answer: B

Explanation:
Regression is the supervised learning task for modeling and predicting continuous, numeric variables. Examples include predicting real-estate prices, stock price movements, or student test scores.
Classification is the supervised learning task for modeling and predicting categorical variables. Examples include predicting employee churn, email spam, financial fraud, or student letter grades.
Clustering is an unsupervised learning task for finding natural groupings of observations (i.e. clusters) based on the inherent structure within your dataset. Examples include customer segmentation, grouping similar items in e-commerce, and social network analysis.
Reference: https://elitedatascience.com/machine-learning-algorithms

NEW QUESTION 23

What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

  • A. Include multiple time series values within the row key
  • B. Keep the row keep as an 8 bit integer
  • C. Keep your row key reasonably short
  • D. Keep your row key as long as the field permits

Answer: C

Explanation:
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

NEW QUESTION 24
......

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