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Google GCP-DE Exam Syllabus Topics:

Google Data Engineer Sample Questions:

1. You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

A) Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
B) Create an authorized view in BigQuery to restrict access to tables with sensitive data.
C) Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention AP
D) Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
E) Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.

2. You have developed three data processing jobs. One executes a Cloud Dataflow pipeline that transforms data uploaded to Cloud Storage and writes results to BigQuery. The second ingests data from on-premises servers and uploads it to Cloud Storage. The third is a Cloud Dataflow pipeline that gets information from third-party data providers and uploads the information to Cloud Storage. You need to be able to schedule and monitor the execution of these three workflows and manually execute them when needed. What should you do?

A) Set up cron jobs in a Compute Engine instance to schedule and monitor the pipelines using GCP API calls.
B) Create a Direct Acyclic Graph in Cloud Composer to schedule and monitor the jobs.
C) Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.
D) Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.

3. You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?

A) Process the generated Entity Analysis as labels.
B) Call the model from your application and process the results as labels.
C) Call the Cloud Natural Language API from your applicatio
D) Process the generated Sentiment Analysis as labels.
E) Deploy the model using Cloud Machine Learning Engin
F) Deploy the model using a KubernetesEngine cluste
G) Call the Cloud Natural Language API from your applicatio
H) Build and train a text classification model using TensorFlo
I) Build and train a text classification model using TensorFlo
J) Call the model from your application and process the results as labels.

4. You want to automate execution of a multi-step data pipeline running on Google Cloud. The pipeline includes Cloud Dataproc and Cloud Dataflow jobs that have multiple dependencies on each other. You want to use managed services where possible, and the pipeline will run every day. Which tool should you use?

A) cron
B) Workflow Templates on Cloud Dataproc
C) Cloud Composer
D) Cloud Scheduler

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

Solutions: