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Your company needs to upload their historic data to Cloud Storage. The security rules don’t allow access from external IPs to their on-premises resources. After an initial upload, they will add new data from existing on-premises applications every day. What should they do?

A.

Execute gsutil rsync from the on-premises servers.

B.

Use Cloud Dataflow and write the data to Cloud Storage.

C.

Write a job template in Cloud Dataproc to perform the data transfer.

D.

Install an FTP server on a Compute Engine VM to receive the files and move them to Cloud Storage.

You need to move 2 PB of historical data from an on-premises storage appliance to Cloud Storage within six months, and your outbound network capacity is constrained to 20 Mb/sec. How should you migrate this data to Cloud Storage?

A.

Use Transfer Appliance to copy the data to Cloud Storage

B.

Use gsutil cp –J to compress the content being uploaded to Cloud Storage

C.

Create a private URL for the historical data, and then use Storage Transfer Service to copy the data to Cloud Storage

D.

Use trickle or ionice along with gsutil cp to limit the amount of bandwidth gsutil utilizes to less than 20 Mb/sec so it does not interfere with the production traffic

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 iscreated

D.

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

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 runsvery 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

Your company receives both batch- and stream-based event data. You want to process the data using Google Cloud Dataflow over a predictable time period. However, you realize that in some instances data can arrive late or out of order. How should you design your Cloud Dataflow pipeline to handle data that is late or out of order?

A.

Set a single global window to capture all the data.

B.

Set sliding windows to capture all the lagged data.

C.

Use watermarks and timestamps to capture the lagged data.

D.

Ensure every datasource type (stream or batch) has a timestamp, and use the timestamps to define the logic for lagged data.

You created an analytics environment on Google Cloud so that your data scientist team can explore data without impacting the on-premises Apache Hadoop solution. The data in the on-premises Hadoop Distributed File System (HDFS) cluster is in Optimized Row Columnar (ORC) formatted files with multiple columns of Hive partitioning. The data scientist team needs to be able to explore the data in a similar way as they used the on-premises HDFS cluster with SQL on the Hive query engine. You need to choose the most cost-effective storage and processing solution. What should you do?

A.

Import the ORC files lo Bigtable tables for the data scientist team.

B.

Import the ORC files to BigOuery tables for the data scientist team.

C.

Copy the ORC files on Cloud Storage, then deploy a Dataproc cluster for the data scientist team.

D.

Copy the ORC files on Cloud Storage, then create external BigQuery tables for the data scientist team.

Your Cloud Storage data lake has raw, processed, and historical data in different buckets. Data older than two years is rarely accessed, and all data must be retained for no longer than seven years. You are concerned about rising storage costs. How should you control costs for the historical data bucket?

A.

Write a script on a Compute Engine instance, triggered daily by Cloud Scheduler, to scan all objects and delete any older than seven years.

B.

Configure an Object Lifecycle Management rule to transition objects older than two years to the Archive storage class and eventually delete them after seven years.

C.

Enable the Autoclass feature on your Cloud Storage buckets and select Opt-in to object transitions to Coldline and Archive storage classes.

D.

Replicate the buckets to a different region with lower storage costs and an Object Lifecycle Management rule to delete objects after seven years.

You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time. What should you do?

A.

Send the data to Google Cloud Datastore and then export to BigQuery.

B.

Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.

C.

Send the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.

D.

Export logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.

You have uploaded 5 years of log data to Cloud Storage A user reported that some data points in the log data are outside of their expected ranges, which indicates errors You need to address this issue and be able to run the process again in the future while keeping the original data for compliance reasons. What should you do?

A.

Import the data from Cloud Storage into BigQuery Create a new BigQuery table, and skip the rows with errors.

B.

Create a Compute Engine instance and create a new copy of the data in Cloud Storage Skip the rows with errors

C.

Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to a new dataset inCloud Storage

D.

Create a Cloud Dataflow workflow that reads the data from Cloud Storage, checks for values outside the expected range, sets the value to an appropriate default, and writes the updated records to the same dataset in Cloud Storage

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.

Threading

B.

Serialization

C.

Dropout Methods

D.

Dimensionality Reduction