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The Google Professional-Data-Engineer exam consists of multiple-choice questions and requires candidates to demonstrate their knowledge of various data engineering concepts and technologies. The questions are designed to test the candidate's ability to analyze data, transform data, and implement data processing solutions using Google Cloud Platform's tools and services. Professional-Data-Engineer exam is two hours long, and candidates must achieve a passing score of 70% or higher to earn the certification.
Google Professional-Data-Engineer Exam is one of the most prestigious and sought-after certifications in the field of data engineering. It is designed to test the knowledge, skills, and expertise of professionals in designing, building, and maintaining data processing systems on the Google Cloud Platform. Professional-Data-Engineer exam is intended for those who have experience in data engineering and are looking to validate their skills and knowledge in this field.
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Here the candidates need to demonstrate their expertise in using pre-built Machine Learning models as a service, including Machine Learning APIs (for instance, Speech API, Vision API, etc.), customizing Machine Learning APIs (for instance, Auto ML text, AutoML Vision, etc.), conversational experiences (for instance, Dialogflow). The applicants should also have the skills in deploying the Machine Learning pipeline. This involves the ability to ingest relevant data, perform retraining of machine learning models (BigQuery ML, Cloud Machine Learning Engine, Spark ML, Kubeflow), as well as execute continuous evaluation. Additionally, the students should be able to choose the relevant training & serving infrastructure as well as know how to fulfill measuring, monitoring, and troubleshooting of Machine Learning models.
NEW QUESTION # 334
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?
Answer: D
NEW QUESTION # 335
You orchestrate ETL pipelines by using Cloud Composer One of the tasks in the Apache Airflow directed acyclic graph (DAG) relies on a third-party service. You want to be notified when the task does not succeed.
What should you do?
Answer: C
Explanation:
By assigning a function with notification logic to the on_failure_callback parameter, you can customize the action that is taken when a task fails in your DAG1. For example, you can send anemail, a Slack message, or a PagerDuty alert to notify yourself or your team about the task failure2. This option is more flexible and reliable than configuring a Cloud Monitoring alert onthe sla_missed metric, which only triggers when a task misses its scheduled deadline3. The sla_miss_callback parameter is also related to the sla_missed metric, and it is executed when the task instance has not succeeded and the time is past the task's scheduled execution date plus its sla4. The on_retry_callback parameter is executed before a task is retried4. These options are not suitable for notifying when a task does not succeed, as they depend on the task's schedule and retry settings, which may not reflect the actual task completion status. References:
1: Callbacks | Cloud Composer | Google Cloud
2: How to Send an Email on Task Failure in Airflow - Astronomer
3: Monitoring SLA misses | Cloud Composer | Google Cloud
4: BaseOperator | Apache Airflow Documentation
NEW QUESTION # 336
You work for a farming company. You have one BigQuery table named sensors, which is about 500 MB and contains the list of your 5000 sensors, with columns for id, name, and location. This table is updated every hour. Each sensor generates one metric every 30 seconds along with a timestamp. which you want to store in BigQuery. You want to run an analytical query on the data once a week for monitoring purposes. You also want to minimize costs. What data model should you use?
Answer: D
Explanation:
For a farming company with a sensor data table updated every 30 seconds, the goal is to minimize costs while facilitating weekly analytical queries. The best data model will effectively manage data storage, update frequency, and query performance.
* Partitioned Metrics Table:
* Creating a metrics table partitioned by timestamp optimizes query performance and storage costs.
* Partitioning by timestamp allows for efficient querying, especially for time-based analyses.
* Sensor ID Reference:
* Including a sensor_id column in the metrics table that points to the id column in the sensors table ensures data normalization.
* This structure avoids redundancy and maintains a clear relationship between sensors and their metrics.
* Using INSERT Statements:
* Using INSERT statements to append new metrics every 30 seconds is efficient and cost-effective.
* INSERT operations are more suitable than UPDATE operations for adding new data entries, especially at high frequencies.
* Joining Tables for Analysis:
* When running analytical queries, joining the partitioned metrics table with the sensors table as needed provides a comprehensive view of the data.
* This approach leverages BigQuery's powerful JOIN capabilities while keeping the data model normalized and efficient.
Google Data Engineer References:
* BigQuery Partitioned Tables
* BigQuery Best Practices
* Efficient Data Partitioning
* BigQuery Data Modeling
Using this data model, the farming company can manage its sensor data effectively, minimize costs, and perform weekly analytical queries with high efficiency.
NEW QUESTION # 337
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.)
Answer: C,D
NEW QUESTION # 338
You have a data pipeline with a Cloud Dataflow job that aggregates and writes time series metrics to Cloud Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take? (Choose two.)
Answer: A,D
NEW QUESTION # 339
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