Google Data Engineer : GCP-DE

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Google Data Engineer Sample Questions:

1. You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt data. What should you do?

A) Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
B) Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.
C) Add a SideInput that returns a Boolean if the element is corrupt.
D) Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.

2. 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 Dataflow to run your transformation
B) Monitor the job system lag with Stackdrive
C) Use Cloud Dataflow to run your transformation
D) Monitor the total execution time for a sampling of job
E) Configure the job to use non-default Compute Engine machine types when needed.
F) Use Cloud Dataproc to run your transformation
G) Use the diagnose command to generate an operational output archiv
H) Use Cloud Dataproc to run your transformation
I) Monitor CPU utilization for the cluste
J) Resize the number of worker nodes in your cluster via the command line.
K) Use the default autoscaling setting for worker instances.
L) Locate the bottleneck and adjust cluster resources.

3. Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

A) Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
B) Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
C) Assemble 1,000 files into a tape archive (TAR) fil
D) Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
E) Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
F) Introduce data compression for each file to increase the rate file of file transfer.

4. 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) Use at least 300 GB of data.
C) Run your test for at least 10 minutes.
D) Before you test, run a heavy pre-test for several minutes.

5. You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this dat a. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

A) Develop a data pipeline where status updates are appended to BigQuery instead of updated.
B) Use BigQuery'ssupport for external data sources to query.
C) Denormalize the data as must as possible.
D) Preserve the structure of the data as much as possible.
E) Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro fil
F) Use BigQuery UPDATE to further reduce the size of the dataset.

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