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Understanding functional and technical aspects of Google Professional Data Engineer Exam Operationalizing machine learning models

The following will be discussed here:

Continuous evaluation
Impact of dependencies of machine learning models
Common sources of error (e.g., assumptions about data)
Ingesting appropriate data
Machine learning terminology (e.g., features, labels, models, regression, classification, recommendation, supervised and unsupervised learning, evaluation metrics)
Customizing ML APIs (e.g., AutoML Vision, Auto ML text)
Conversational experiences (e.g., Dialogflow)
Distributed vs. single machine
Use of edge compute
Hardware accelerators (e.g., GPU, TPU)
Deploying an ML pipeline

Google Professional-Data-Engineer Certification Exam is a rigorous exam designed to test a candidate's skills in designing and implementing data processing systems on Google Cloud Platform. Professional-Data-Engineer exam covers a broad range of topics including data processing, data analysis, data visualization, and machine learning. Professional-Data-Engineer exam also includes questions on data security, compliance, and privacy. Professional-Data-Engineer exam format includes multiple choice and scenario-based questions, which test a candidate's ability to apply their knowledge in real-world situations.

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A steadily rising competition has been noted in the tech field. Countless candidates around the globe aspire to be Google Certified Professional Data Engineer Exam in this field. Once you become Google certified, a whole new scope opens up to you and you are immediately hired by reputed firms. Even though the Google Certified Professional Data Engineer Exam certification boosts your career options, you have to pass the Professional-Data-Engineer Exam.

Google Professional-Data-Engineer Certification Exam comprises of multiple-choice and multiple-select questions that require a thorough understanding of Google Cloud Platform services such as BigQuery, Google Cloud Storage, and Google Cloud Dataflow. Professional-Data-Engineer exam also tests an individual's knowledge of data processing patterns and best practices, understanding of machine learning models and algorithms, and proficiency in designing and deploying solutions that meet business requirements.

Google Certified Professional Data Engineer Exam Sample Questions (Q328-Q333):

NEW QUESTION # 328
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. Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.
B. Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.
C. Store the data in a regional Cloud Storage bucket. Aceess the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.
D. Store the data in BigQuery. Access the data using the BigQuery Connector or Cloud Dataproc and Compute Engine.

Answer: A

NEW QUESTION # 329
When running a pipeline that has a BigQuery source, on your local machine, you continue to get permission denied errors. What could be the reason for that?

A. Your gcloud does not have access to the BigQuery resources
B. You are missing gcloud on your machine
C. Pipelines cannot be run locally
D. BigQuery cannot be accessed from local machines

Answer: A

Explanation:
When reading from a Dataflow source or writing to a Dataflow sink using DirectPipelineRunner, the Cloud Platform account that you configured with the gcloud executable will need access to the corresponding source
/sink
Reference: https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners
/DirectPipelineRunner

NEW QUESTION # 330
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select
2 answers)?

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

Answer: B,D

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 # 331
You need to create a SQL pipeline. The pipeline runs an aggregate SOL transformation on a BigQuery table every two hours and appends the result to another existing BigQuery table. You need to configure the pipeline to retry if errors occur. You want the pipeline to send an email notification after three consecutive failures.
What should you do?

A. Use the BigQueryUpsertTableOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter to true.
B. Create a BigQuery scheduled query to run the SQL transformation with schedule options that repeats every two hours, and enable notification to Pub/Sub topic. Use Pub/Sub and Cloud Functions to send an email after three tailed executions.
C. Create a BigQuery scheduled query to run the SOL transformation with schedule options that repeats every two hours, and enable email notifications.
D. Use the BigQuerylnsertJobOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter to true.

Answer: B

Explanation:
To create a robust and resilient SQL pipeline in BigQuery that handles retries and failure notifications, consider the following:
* BigQuery Scheduled Queries: This feature allows you to schedule recurring queries in BigQuery. It is a straightforward way to run SQL transformations on a regular basis without requiring extensive setup.
* Error Handling and Retries: While BigQuery Scheduled Queries can run at specified intervals, they don't natively support complex retry logic or failure notifications directly. This is where additional Google Cloud services like Pub/Sub and Cloud Functions come into play.
* Pub/Sub for Notifications: By configuring a BigQuery scheduled query to publish messages to a Pub
/Sub topic upon failure, you can create a decoupled and scalable notification system.
* Cloud Functions: Cloud Functions can subscribe to the Pub/Sub topic and implement logic to count consecutive failures. After detecting three consecutive failures, the Cloud Function can then send an email notification using a service like SendGrid or Gmail API.
Implementation Steps:
* Set up a BigQuery Scheduled Query:
* Create a scheduled query in BigQuery to run your SQL transformation every two hours.
* Configure the scheduled query to publish a notification to a Pub/Sub topic in case of a failure.
* Create a Pub/Sub Topic:
* Create a Pub/Sub topic that will receive messages from the scheduled query.
* Develop a Cloud Function:
* Write a Cloud Function that subscribes to the Pub/Sub topic.
* Implement logic in the Cloud Function to track failure messages. If three consecutive failure messages are detected, the function sends an email notification.
Reference Links:
* BigQuery Scheduled Queries
* Pub/Sub Documentation
* Cloud Functions Documentation
* SendGrid Email API
* Gmail API

NEW QUESTION # 332
You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don't get slots to execute their query and you need to correct this. You'd like to avoid introducing new projects to your account.
What should you do?

A. Create an additional project to overcome the 2K on-demand per-project quota.
B. Convert your batch BQ queries into interactive BQ queries.
C. Switch to flat-rate pricing and establish a hierarchical priority model for your projects.
D. Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.

Answer: C

Explanation:
Explanation
Reference https://cloud.google.com/blog/products/gcp/busting-12-myths-about-bigquery

NEW QUESTION # 333
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