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The Professional Data Engineer exam is the industry-standard exam that proves the candidate’s ability to do data-driven decision-making by assembling, transforming, and publishing data. If you are rooting for a career in data engineering, you should take this test. It will lead you to attain the Professional Data Engineer certification issued by Google.

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The Google Professional-Data-Engineer Exam is designed for professionals who have experience working with data processing systems in a business environment. Candidates should have a thorough understanding of data modeling, data warehousing, and data transformation. Additionally, they should have experience working with cloud-based data storage and processing systems such as Google Cloud Platform, AWS, or Microsoft Azure.

Google Certified Professional Data Engineer Exam Sample Questions (Q162-Q167):

NEW QUESTION # 162
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

A. Preemptible workers cannot store data.
B. A Dataproc cluster cannot have only preemptible workers.
C. Preemptible workers cannot use persistent disk.
D. If a preemptible worker is reclaimed, then a replacement worker must be added manually.

Answer: A,B

Explanation:
Explanation
The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster:
Processing only-Since preemptibles can be reclaimed at any time, preemptible workers do not store data.
Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
No preemptible-only clusters-To ensure clusters do not lose all workers, Cloud Dataproc cannot create preemptible-only clusters.
Persistent disk size-As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits.
Reference: https://cloud.google.com/dataproc/docs/concepts/preemptible-vms

NEW QUESTION # 163
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics.
Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

A. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
B. Add capacity (memory and disk space) to the database server by the order of 200.
C. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
D. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.

Answer: D

Explanation:
Explanation

NEW QUESTION # 164
You want to create a machine learning model using BigQuery ML and create an endpoint foe hosting the model using Vertex Al. This will enable the processing of continuous streaming data in near-real time from multiple vendors. The data may contain invalid values. What should you do?

A. Create a Pub/Sub topic and send all vendor data to it Use Dataflow to process and sanitize the Pub/Sub data and stream it to BigQuery.
B. Create a Pub'Sub topic and send all vendor data to it Connect a Cloud Function to the topic to process the data and store it in BigQuery.
C. Create a new BigOuery dataset and use streaming inserts to land the data from multiple vendors.
Configure your BigQuery ML model to use the "ingestion' dataset as the training data.
D. Use BigQuery streaming inserts to land the data from multiple vendors whore your BigQuery dataset ML model is deployed.

Answer: A

Explanation:
Dataflow provides a scalable and flexible way to process and clean the incoming data in real-time before loading it into BigQuery.

NEW QUESTION # 165
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. Develop an App Engine application to schedule and request the status of the jobs using GCP API calls.
D. Use Stackdriver Monitoring and set up an alert with a Webhook notification to trigger the jobs.

Answer: B

Explanation:
Cloud composer is used to schedule the interdependent jobs.

NEW QUESTION # 166
Your company's customer and order databases are often under heavy load. This makes performing analytics against them difficult without harming operations. The databases are in a MySQL cluster, with nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations.
What should you do?

A. Add a node to the MySQL cluster and build an OLAP cube there.
B. Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc.
C. Connect an on-premises Apache Hadoop cluster to MySQL and perform ETL.
D. Use an ETL tool to load the data from MySQL into Google BigQuery.

Answer: C

NEW QUESTION # 167
......

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