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NEW QUESTION 1

After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You’ve loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison.
What should you do?

• A. Select random samples from the tables using the RAND() function and compare the samples.
• B. Select random samples from the tables using the HASH() function and compare the samples.
• C. Use a Dataproc cluster and the BigQuery Hadoop connector to read the data from each table and calculate a hash from non-timestamp columns of the table after sortin
• D. Compare the hashes of each table.
• E. Create stratified random samples using the OVER() function and compare equivalent samples from each table.

Answer: B

NEW QUESTION 2

If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

• A. Do not use a production instance.
• B. Run your test for at least 10 minutes.
• C. Before you test, run a heavy pre-test for several minutes.
• D. Use at least 300 GB of data.

Answer: A

Explanation:
If you're running a performance test that depends upon Cloud Bigtable, be sure to follow these steps as you
plan and execute your test:
Use a production instance. A development instance will not give you an accurate sense of how a production instance performs under load.
Use at least 300 GB of data. Cloud Bigtable performs best with 1 TB or more of data. However, 300 GB of data is enough to provide reasonable results in a performance test on a 3-node cluster. On larger clusters, use 100 GB of data per node.
Before you test, run a heavy pre-test for several minutes. This step gives Cloud Bigtable a chance to balance data across your nodes based on the access patterns it observes.
Run your test for at least 10 minutes. This step lets Cloud Bigtable further optimize your data, and it helps ensure that you will test reads from disk as well as cached reads from memory.
Reference: https://cloud.google.com/bigtable/docs/performance

NEW QUESTION 3

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. Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.
• B. Add a new column called FullName to the Users tabl
• C. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.
• D. 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.
• E. Use BigQuery to export the data for the table to a CSV fil
• F. 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 FullNam
• G. Run a BigQuery load job to load the new CSV file into BigQuery.

Answer: C

NEW QUESTION 4

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 SQL
• B. Cloud Bigtable
• C. Cloud Spanner
• D. Cloud Datastore

Answer: A

NEW QUESTION 5

Which of these numbers are adjusted by a neural network as it learns from a training dataset (select 2 answers)?

• A. Weights
• B. Biases
• C. Continuous features
• D. Input values

Answer: AB

Explanation:
A neural network is a simple mechanism that’s implemented with basic math. The only difference between the traditional programming model and a neural network is that you let the computer determine the parameters (weights and bias) by learning from training datasets.
Reference:
https://cloud.google.com/blog/big-data/2016/07/understanding-neural-networks-with-tensorflow-playground

NEW QUESTION 6

A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to implement a change that would improve query performance in BigQuery. What should you do?

• A. Implement clustering in BigQuery on the ingest date column.
• B. Implement clustering in BigQuery on the package-tracking ID column.
• C. Tier older data onto Cloud Storage files, and leverage extended tables.
• D. Re-create the table using data partitioning on the package delivery date.

Answer: A

NEW QUESTION 7

Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the data. How should you deduplicate the data most efficiency?

• A. Assign global unique identifiers (GUID) to each data entry.
• B. Compute the hash value of each data entry, and compare it with all historical data.
• C. Store each data entry as the primary key in a separate database and apply an index.
• D. Maintain a database table to store the hash value and other metadata for each data entry.

Answer: D

NEW QUESTION 8

You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the shade of each dot represents what class it is. You want to classify this data accurately using a linear algorithm.

To do this you need to add a synthetic feature. What should the value of that feature be?

• A. X^2+Y^2
• B. X^2
• C. Y^2
• D. cos(X)

Answer: D

NEW QUESTION 9

Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?

• A. Issue a command to restart the database servers.
• B. Retry the query with exponential backoff, up to a cap of 15 minutes.
• C. Retry the query every second until it comes back online to minimize staleness of data.
• D. Reduce the query frequency to once every hour until the database comes back online.

Answer: B

NEW QUESTION 10

Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the cost of queries, your organization created a view called events, which queries only the last 14 days of data. The view is described in legacy SQL. Next month, existing applications will be connecting to BigQuery to read the events data via an ODBC connection. You need to ensure the applications can connect. Which two actions should you take? (Choose two.)

• A. Create a new view over events using standard SQL
• B. Create a new partitioned table using a standard SQL query
• C. Create a new view over events_partitioned using standard SQL
• D. Create a service account for the ODBC connection to use for authentication
• E. Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connectionand shared “events”

Answer: AE

NEW QUESTION 11

Your company’s on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?

• A. Put the data into Google Cloud Storage.
• B. Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.
• C. Tune the Cloud Dataproc cluster so that there is just enough disk for all data.
• D. Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.

Answer: B

NEW QUESTION 12

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 13

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

• A. Apache Spark
• B. Apache Hadoop
• C. Apache Kafka
• D. Apache Beam

Answer: D

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

NEW QUESTION 14

Your company maintains a hybrid deployment with GCP, where analytics are performed on your anonymized customer data. The data are imported to Cloud Storage from your data center through parallel uploads to a data transfer server running on GCP. Management informs you that the daily transfers take too long and have asked you to fix the problem. You want to maximize transfer speeds. Which action should you take?

• A. Increase the CPU size on your server.
• B. Increase the size of the Google Persistent Disk on your server.
• C. Increase your network bandwidth from your datacenter to GCP.
• D. Increase your network bandwidth from Compute Engine to Cloud Storage.

Answer: C

NEW QUESTION 15

Which of these statements about BigQuery caching is true?

• A. By default, a query's results are not cached.
• B. BigQuery caches query results for 48 hours.
• C. Query results are cached even if you specify a destination table.
• D. There is no charge for a query that retrieves its results from cache.

Answer: D

Explanation:
When query results are retrieved from a cached results table, you are not charged for the query. BigQuery caches query results for 24 hours, not 48 hours.
Query results are not cached if you specify a destination table.
A query's results are always cached except under certain conditions, such as if you specify a destination table. Reference: https://cloud.google.com/bigquery/querying-data#query-caching

NEW QUESTION 16

Does Dataflow process batch data pipelines or streaming data pipelines?

• A. Only Batch Data Pipelines
• B. Both Batch and Streaming Data Pipelines
• C. Only Streaming Data Pipelines
• D. None of the above

Answer: B

Explanation:
Dataflow is a unified processing model, and can execute both streaming and batch data pipelines Reference: https://cloud.google.com/dataflow/

NEW QUESTION 17

By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

• A. Windows at every 100 MB of data
• B. Single, Global Window
• C. Windows at every 1 minute
• D. Windows at every 10 minutes

Answer: B

Explanation:
Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections
Reference: https://cloud.google.com/dataflow/model/pcollection

NEW QUESTION 18

You have historical data covering the last three years in BigQuery and a data pipeline that delivers new data to BigQuery daily. You have noticed that when the Data Science team runs a query filtered on a date column and limited to 30–90 days of data, the query scans the entire table. You also noticed that your bill is increasing more quickly than you expected. You want to resolve the issue as cost-effectively as possible while maintaining the ability to conduct SQL queries. What should you do?

• A. Re-create the tables using DD
• B. Partition the tables by a column containing a TIMESTAMP or DATE Type.
• C. Recommend that the Data Science team export the table to a CSV file on Cloud Storage and use Cloud Datalab to explore the data by reading the files directly.
• D. Modify your pipeline to maintain the last 30–90 days of data in one table and the longer history in a different table to minimize full table scans over the entire history.
• E. Write an Apache Beam pipeline that creates a BigQuery table per da
• F. Recommend that the Data Science team use wildcards on the table name suffixes to select the data they need.

Answer: C

NEW QUESTION 19

You are designing a cloud-native historical data processing system to meet the following conditions:
The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
A streaming data pipeline stores new data daily.
Peformance is not a factor in the solution.
The solution design should maximize availability.
How should you design data storage for this solution?

• A. Create a Cloud Dataproc cluster with high availabilit
• B. Store the data in HDFS, and peform analysis as needed.
• C. Store the data in BigQuer
• D. Access the data using the BigQuery Connector or Cloud Dataproc and Compute Engine.
• E. Store the data in a regional Cloud Storage bucke
• F. Aceess the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.
• G. Store the data in a multi-regional Cloud Storage bucke
• H. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.

Answer: C

NEW QUESTION 20

You are planning to migrate your current on-premises Apache Hadoop deployment to the cloud. You need to ensure that the deployment is as fault-tolerant and cost-effective as possible for long-running batch jobs. You want to use a managed service. What should you do?

• A. Deploy a Cloud Dataproc cluste
• B. Use a standard persistent disk and 50% preemptible worker
• C. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
• D. Deploy a Cloud Dataproc cluste
• E. Use an SSD persistent disk and 50% preemptible worker
• F. Store data in Cloud Storage, and change references in scripts from hdfs:// to gs://
• G. Install Hadoop and Spark on a 10-node Compute Engine instance group with standard instance
• H. Install the Cloud Storage connector, and store the data in Cloud Storag
• I. Change references in scripts from hdfs:// to gs://
• J. Install Hadoop and Spark on a 10-node Compute Engine instance group with preemptible instances.Store data in HDF
• K. Change references in scripts from hdfs:// to gs://

Answer: A

NEW QUESTION 21

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. BigQuery
• B. Cloud Bigtable
• C. Cloud Datastore
• D. Cloud SQL for PostgreSQL

Answer: A

NEW QUESTION 22

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

Answer: A

Explanation:
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

NEW QUESTION 23

A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL ‘dataset.model’, table user_features). How should you create the ML pipeline?

• A. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
• B. Create an Authorized View with the provided quer
• C. Share the dataset that contains the view with the application service account.
• D. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the quer
• E. Grant the Dataflow Worker role to the application service account.
• F. Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query.Write the results to Cloud Bigtable using BigtableI
• G. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.

Answer: D

NEW QUESTION 24
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