Google Professional Data Engineer Exam Questions Instant Access

Question 1

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?

AStore your data in Bigtable Concatenate the sensor ID and timestamp and use it as the row key Perform an export to BigQuery every day.

BStore your data in BigQuery Concatenate the sensor ID and timestamp. and use it as the primary key.

CStore your data in Bigtable Concatenate the sensor ID and metric, and use it as the row key Perform an export to BigQuery every day.

DStore your data in BigQuery. Use the metric as a primary key.

Answer : A

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.

Question 2

Which of the following is NOT a valid use case to select HDD (hard disk drives) as the storage for Google Cloud Bigtable?

AYou expect to store at least 10 TB of data.

BYou will mostly run batch workloads with scans and writes, rather than frequently executing random reads of a small number of rows.

CYou need to integrate with Google BigQuery.

DYou will not use the data to back a user-facing or latency-sensitive application.

Answer : C

For example, if you plan to store extensive historical data for a large number of remote-sensing devices and then use the data to generate daily reports, the cost savings for HDD storage may justify the performance tradeoff. On the other hand, if you plan to use the data to display a real-time dashboard, it probably would not make sense to use HDD storage---reads would be much more frequent in this case, and reads are much slower with HDD storage.

Question 3

When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?

A500 TB

B1 GB

C1 TB

D500 GB

Answer : C

Cloud Bigtable is not a relational database. It does not support SQL queries, joins, or multi-row transactions. It is not a good solution for less than 1 TB of data.

Question 4

You need to choose a database for a new project that has the following requirements:

Fully managed

Able to automatically scale up

Transactionally consistent

Able to scale up to 6 TB

Able to be queried using SQL

Which database do you choose?

ACloud SQL

BCloud Bigtable

CCloud Spanner

DCloud Datastore

Answer : C

Question 5

You have designed an Apache Beam processing pipeline that reads from a Pub/Sub topic. The topic has a message retention duration of one day, and writes to a Cloud Storage bucket. You need to select a bucket location and processing strategy to prevent data loss in case of a regional outage with an RPO of 15 minutes. What should you do?

A1 Use a regional Cloud Storage bucket2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs3 Seek the subscription back in time by one day to recover the acknowledged messages4 Start the Dataflow job in a secondary region and write in a bucket in the same region

B1 Use a multi-regional Cloud Storage bucket2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs3 Seek the subscription back in time by 60 minutes to recover the acknowledged messages4 Start the Dataflow job in a secondary region

C1. Use a dual-region Cloud Storage bucket.2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs3 Seek the subscription back in time by 15 minutes to recover the acknowledged messages4 Start the Dataflow job in a secondary region

D1. Use a dual-region Cloud Storage bucket with turbo replication enabled2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs3 Seek the subscription back in time by 60 minutes to recover the acknowledged messages4 Start the Dataflow job in a secondary region.

Answer : C

A dual-region Cloud Storage bucket is a type of bucket that stores data redundantly across two regions within the same continent. This provides higher availability and durability than a regional bucket, which stores data in a single region. A dual-region bucket also provides lower latency and higher throughput than a multi-regional bucket, which stores data across multiple regions within a continent or across continents. A dual-region bucket with turbo replication enabled is a premium option that offers even faster replication across regions, but it is more expensive and not necessary for this scenario.

By using a dual-region Cloud Storage bucket, you can ensure that your data is protected from regional outages, and that you can access it from either region with low latency and high performance. You can also monitor the Dataflow metrics with Cloud Monitoring to determine when an outage occurs, and seek the subscription back in time by 15 minutes to recover the acknowledged messages. Seeking a subscription allows you to replay the messages from a Pub/Sub topic that were published within the message retention duration, which is one day in this case. By seeking the subscription back in time by 15 minutes, you can meet the RPO of 15 minutes, which means the maximum amount of data loss that is acceptable for your business. You can then start the Dataflow job in a secondary region and write to the same dual-region bucket, which will resume the processing of the messages and prevent data loss.

Option A is not a good solution, as using a regional Cloud Storage bucket does not provide any redundancy or protection from regional outages. If the region where the bucket is located experiences an outage, you will not be able to access your data or write new data to the bucket. Seeking the subscription back in time by one day is also unnecessary and inefficient, as it will replay all the messages from the past day, even though you only need to recover the messages from the past 15 minutes.

Option B is not a good solution, as using a multi-regional Cloud Storage bucket does not provide the best performance or cost-efficiency for this scenario. A multi-regional bucket stores data across multiple regions within a continent or across continents, which provides higher availability and durability than a dual-region bucket, but also higher latency and lower throughput. A multi-regional bucket is more suitable for serving data to a global audience, not for processing data with Dataflow within a single continent. Seeking the subscription back in time by 60 minutes is also unnecessary and inefficient, as it will replay more messages than needed to meet the RPO of 15 minutes.

Option D is not a good solution, as using a dual-region Cloud Storage bucket with turbo replication enabled does not provide any additional benefit for this scenario, but only increases the cost. Turbo replication is a premium option that offers faster replication across regions, but it is not required to meet the RPO of 15 minutes. Seeking the subscription back in time by 60 minutes is also unnecessary and inefficient, as it will replay more messages than needed to meet the RPO of 15 minutes.Reference:Storage locations | Cloud Storage | Google Cloud,Dataflow metrics | Cloud Dataflow | Google Cloud,Seeking a subscription | Cloud Pub/Sub | Google Cloud,Recovery point objective (RPO) | Acronis.

Question 6

You are migrating an application that tracks library books and information about each book, such as author or year published, from an on-premises data warehouse to BigQuery In your current relational database, the author information is kept in a separate table and joined to the book information on a common key Based on Google's recommended practice for schema design, how would you structure the data to ensure optimal speed of queries about the author of each book that has been borrowed?

AKeep the schema the same, maintain the different tables for the book and each of the attributes, and query as you are doing today

BCreate a table that is wide and includes a column for each attribute, including the author's first name, last name, date of birth, etc

CCreate a table that includes information about the books and authors, but nest the author fields inside the author column

DKeep the schema the same, create a view that joins all of the tables, and always query the view

Answer : C

Question 7

You work for a shipping company that has distribution centers where packages move on delivery lines to route them properly. The company wants to add cameras to the delivery lines to detect and track any visual damage to the packages in transit. You need to create a way to automate the detection of damaged packages and flag them for human review in real time while the packages are in transit. Which solution should you choose?

AUse BigQuery machine learning to be able to train the model at scale, so you can analyze the packages in batches.

BTrain an AutoML model on your corpus of images, and build an API around that model to integrate with the package tracking applications.

CUse the Cloud Vision API to detect for damage, and raise an alert through Cloud Functions. Integrate the package tracking applications with this function.

DUse TensorFlow to create a model that is trained on your corpus of images. Create a Python notebook in Cloud Datalab that uses this model so you can analyze for damaged packages.

Answer : A

Google Cloud Certified Professional Data Engineer Exam Questions