PROFESSIONAL-DATA-ENGINEER TEST QUESTION & PROFESSIONAL-DATA-ENGINEER DUMPS PDF

Professional-Data-Engineer Test Question & Professional-Data-Engineer Dumps PDF

Professional-Data-Engineer Test Question & Professional-Data-Engineer Dumps PDF

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Our Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) PDF dumps format contains Google Professional-Data-Engineer questions that are real and updated. You can print these Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) questions if you prefer an off-screen study. Otherwise, you can use this Professional-Data-Engineer PDF document from any location via your laptops, tablets, and smartphones. Time restrictions do not halt Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) exam preparation as you can use Google Certified Professional Data Engineer Exam (Professional-Data-Engineer) exam dumps pdf whenever you have free time.

Prerequisites

There are no formal requirements that the candidates need to meet to qualify for the Google Professional Data Engineer certification. However, without some level of professional experience, it will be difficult for the students to ace the qualifying test. The target individuals are recommended to have three or more years of industry experience, including one or more years of experience in designing and managing solutions with the help of Google Cloud Platform. It is preferable that the applicants also possess some basic database knowledge.

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Google Certified Professional Data Engineer Exam Sample Questions (Q126-Q131):

NEW QUESTION # 126
You've migrated a Hadoop job from an on-premises cluster to Dataproc and Good Storage. Your Spark job is a complex analytical workload fiat consists of many shuffling operations, and initial data are parquet toes (on average 200-400 MB size each) You see some degradation in performance after the migration to Dataproc so you'd like to optimize for it. Your organization is very cost-sensitive so you'd Idee to continue using Dataproc on preemptibles (with 2 non-preemptibles workers only) for this workload. What should you do?

  • A. Increase the see of your parquet files to ensure them to be 1 GB minimum
  • B. Switch from HDDs to SSDs. copy initial data from Cloud Storage to Hadoop Distributed File System (HDFS) run the Spark job and copy results back to Cloud Storage
  • C. Switch to TFRecords format (appr 200 MB per We) instead of parquet files
  • D. Switch from HODs to SSDs override the preemptible VMs configuration to increase the boot disk size

Answer: D


NEW QUESTION # 127
You need to look at BigQuery data from a specific table multiple times a day. The underlying table you are querying is several petabytes in size, but you want to filter your data and provide simple aggregations to downstream users. You want to run queries faster and get up-to-date insights quicker. What should you do?

  • A. Limit the query columns being pulled in the final result.
  • B. Run a scheduled query to pull the necessary data at specific intervals daily.
  • C. Use a cached query to accelerate time to results.
  • D. Create a materialized view based off of the query being run.

Answer: B

Explanation:
Materialized views are precomputed views that periodically cache the results of a query for increased performance and efficiency. BigQuery leverages precomputed results from materialized views and whenever possible reads only changes from the base tables to compute up-to-date results. Materialized views can significantly improve the performance of workloads that have the characteristic of common and repeated queries. Materialized views can also optimize queries with high computation cost and small dataset results, such as filtering and aggregating large tables. Materialized views are refreshed automatically when the base tables change, so they always return fresh data. Materialized views can also be used by the BigQuery optimizer to process queries to the base tables, if any part of the query can be resolved by querying the materialized view. Reference:
Introduction to materialized views
Create materialized views
BigQuery Materialized View Simplified: Steps to Create and 3 Best Practices Materialized view in Bigquery


NEW QUESTION # 128
You have a network of 1000 sensors. The sensors generate time series data: one metric per sensor per second, along with a timestamp. You already have 1 TB of data, and expect the data to grow by 1 GB every day You need to access this data in two ways. The first access pattern requires retrieving the metric from one specific sensor stored at a specific timestamp, with a median single-digit millisecond latency. The second access pattern requires running complex analytic queries on the data, including joins, once a day. How should you store this data?

  • A. Store your data in Bigtable Concatenate the sensor ID and metric, and use it as the row key Perform an export to BigQuery every day.
  • B. Store your data in Bigtable Concatenate the sensor ID and timestamp and use it as the row key Perform an export to BigQuery every day.
  • C. Store your data in BigQuery. Use the metric as a primary key.
  • D. Store your data in BigQuery Concatenate the sensor ID and timestamp. and use it as the primary key.

Answer: B

Explanation:
To store your data in a way that meets both access patterns, you should:
* A. Store your data in Bigtable Concatenate the sensor ID and timestamp and use it as the row key Perform an export to BigQuery every day. This option allows you to leverage the high performance and scalability of Bigtable for low-latency point queries on sensor data, as well as the powerful analytics capabilities of BigQuery for complex queries on large datasets. By using the sensor ID and timestamp as the row key, you can ensure that your data is sorted and distributed evenly across Bigtable nodes, and that you can easily retrieve the metric for a specific sensor and time. By performing an export to BigQuery every day, you can transfer your data to a columnar storage format that is optimized for analytical queries, and take advantage of BigQuery's features such as partitioning, clustering, and caching.
* B. Store your data in BigQuery Concatenate the sensor ID and timestamp. and use it as the primary key. This option is not optimal because BigQuery is not designed for low-latency point queries, and using a concatenated primary key may result in poor performance and high costs. BigQuery does not support primary keys natively, and you would have to use a unique constraint or a hash function to enforce uniqueness. Moreover, BigQuery charges by the amount of data scanned, so using a long and complex primary key may increase the query cost and complexity.
* C. Store your data in Bigtable Concatenate the sensor ID and metric, and use it as the row key Perform an export to BigQuery every day. This option is not optimal because using the sensor ID and metric as the row key may result in data skew and hotspots in Bigtable, as some sensors may generate more metrics than others, or some metrics may be more common than others. This may affect the performance and availability of Bigtable, as well as the efficiency of the export to BigQuery.
* D. Store your data in BigQuery. Use the metric as a primary key. This option is not optimal because using the metric as a primary key may result in data duplication and inconsistency in BigQuery, as multiple sensors may generate the same metric at different times, or the same sensor may generate different metrics at the same time. This may affect the accuracy and reliability of your analytical queries, as well as the query cost and complexity.


NEW QUESTION # 129
When creating a new Cloud Dataproc cluster with the projects.regions.clusters.create operation, these four values are required: project, region, name, and ____.

  • A. label
  • B. type
  • C. node
  • D. zone

Answer: D

Explanation:
At a minimum, you must specify four values when creating a new cluster with the projects.regions.clusters.create operation:
The project in which the cluster will be created
The region to use
The name of the cluster
The zone in which the cluster will be created
You can specify many more details beyond these minimum requirements. For example, you can
also specify the number of workers, whether preemptible compute should be used, and the network settings.


NEW QUESTION # 130
You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity 'Movie' the property 'actors' and the property 'tags' have multiple values but the property 'date released' does not. A typical query would ask for all movies with actor=<actorname> ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

  • A. Option A
  • B. Option D
  • C. Option B.
  • D. Option C

Answer: A


NEW QUESTION # 131
......

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