Google Professional-Data-Engineer Exam Practice Test Instant Access

Question 1

You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a

machine-learning process. You want to support a logistic regression model. You also need to monitor and

adjust for null values, which must remain real-valued and cannot be removed. What should you do?

AUse Cloud Dataprep to find null values in sample source data. Convert all nulls to 'none' using a Cloud
Dataproc job.

BUse Cloud Dataprep to find null values in sample source data. Convert all nulls to 0 using a Cloud
Dataprep job.

CUse Cloud Dataflow to find null values in sample source data. Convert all nulls to 'none' using a Cloud
Dataprep job.

DUse Cloud Dataflow to find null values in sample source data. Convert all nulls to using a custom script.

Answer : C

Question 2

You work for a large real estate firm and are preparing 6 TB of home sales data lo be used for machine learning You will use SOL to transform the data and use BigQuery ML lo create a machine learning model. You plan to use the model for predictions against a raw dataset that has not been transformed. How should you set up your workflow in order to prevent skew at prediction time?

AWhen creating your model, use BigQuerys TRANSFORM clause to define preprocessing stops. At prediction time, use BigQuery's ML. EVALUATE clause without specifying any transformations on the raw input data.

BWhen creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps Before requesting predictions, use a saved query to transform your raw input data, and then use ML. EVALUATE

CUse a BigOuery to define your preprocessing logic. When creating your model, use the view as your model training data. At prediction lime, use BigQuery's ML EVALUATE clause without specifying any transformations on the raw input data.

DPreprocess all data using Dataflow. At prediction time, use BigOuery's ML. EVALUATE clause without specifying any further transformations on the input data.

Answer : A

https://cloud.google.com/bigquery-ml/docs/bigqueryml-transform Using the TRANSFORM clause, you can specify all preprocessing during model creation. The preprocessing is automatically applied during the prediction and evaluation phases of machine learning

Question 3

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?

ARun a scheduled query to pull the necessary data at specific intervals daily.

BCreate a materialized view based off of the query being run.

CUse a cached query to accelerate time to results.

DLimit the query columns being pulled in the final result.

Answer : B

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

Question 4

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 bucket
2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs
3 Seek the subscription back in time by one day to recover the acknowledged messages
4 Start the Dataflow job in a secondary region and write in a bucket in the same region

B1 Use a multi-regional Cloud Storage bucket
2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs
3 Seek the subscription back in time by 60 minutes to recover the acknowledged messages
4 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 occurs
3 Seek the subscription back in time by 15 minutes to recover the acknowledged messages
4 Start the Dataflow job in a secondary region

D1. Use a dual-region Cloud Storage bucket with turbo replication enabled
2 Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs
3 Seek the subscription back in time by 60 minutes to recover the acknowledged messages
4 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 5

You have 100 GB of data stored in a BigQuery table. This data is outdated and will only be accessed one or two times a year for analytics with SQL. For backup purposes, you want to store this data to be immutable for 3 years. You want to minimize storage costs. What should you do?

A1 Create a BigQuery table clone.
2. Query the clone when you need to perform analytics.

B1 Create a BigQuery table snapshot.
2 Restore the snapshot when you need to perform analytics.

C1. Perform a BigQuery export to a Cloud Storage bucket with archive storage class.
2 Enable versionmg on the bucket.
3. Create a BigQuery external table on the exported files.

D1 Perform a BigQuery export to a Cloud Storage bucket with archive storage class.
2 Set a locked retention policy on the bucket.
3. Create a BigQuery external table on the exported files.

Answer : D

This option will allow you to store the data in a low-cost storage option, as the archive storage class has the lowest price per GB among the Cloud Storage classes. It will also ensure that the data is immutable for 3 years, as the locked retention policy prevents the deletion or overwriting of the data until the retention period expires. You can still query the data using SQL by creating a BigQuery external table that references the exported files in the Cloud Storage bucket. Option A is incorrect because creating a BigQuery table clone will not reduce the storage costs, as the clone will have the same size and storage class as the original table. Option B is incorrect because creating a BigQuery table snapshot will also not reduce the storage costs, as the snapshot will have the same size and storage class as the original table. Option C is incorrect because enabling versioning on the bucket will not make the data immutable, as the versions can still be deleted or overwritten by anyone with the appropriate permissions. It will also increase the storage costs, as each version of the file will be charged separately.Reference:

Exporting table data | BigQuery | Google Cloud

Storage classes | Cloud Storage | Google Cloud

Retention policies and retention periods | Cloud Storage | Google Cloud

Federated queries | BigQuery | Google Cloud

Question 6

You work for a large ecommerce company. You store your customers order data in Bigtable. You have a garbage collection policy set to delete the data after 30 days and the number of versions is set to 1. When the data analysts run a query to report total customer spending, the analysts sometimes see customer data that is older than 30 days. You need to ensure that the analysts do not see customer data older than 30 days while minimizing cost and overhead. What should you do?

ASet the expiring values of the column families to 30 days and set the number of versions to 2.

BUse a timestamp range filter in the query to fetch the customer's data for a specific range.

CSet the expiring values of the column families to 29 days and keep the number of versions to 1.

DSchedule a job daily to scan the data in the table and delete data older than 30 days.

Answer : B

By using a timestamp range filter in the query, you can ensure that the analysts only see the customer data that is within the desired time range, regardless of the garbage collection policy1. This option is the most cost-effective and simple way to avoid fetching data that is marked for deletion by garbage collection, as it does not require changing the existing policy or creating additional jobs.You can use the Bigtable client libraries or the cbt CLI to apply a timestamp range filter to your read requests2.

Option A is not effective, as it increases the number of versions to 2, which may cause more data to be retained and increase the storage costs. Option C is not reliable, as it reduces the expiring values to 29 days, which may not match the actual data arrival and usage patterns. Option D is not efficient, as it requires scheduling a job daily to scan and delete the data, which may incur additional overhead and complexity.Moreover, none of these options guarantee that the data older than 30 days will be immediately deleted, as garbage collection is an asynchronous process that can take up to a week to remove the data3.Reference:

1: Filters | Cloud Bigtable Documentation | Google Cloud

2: Read data | Cloud Bigtable Documentation | Google Cloud

3: Garbage collection overview | Cloud Bigtable Documentation | Google Cloud

Question 7

You are configuring networking for a Dataflow job. The data pipeline uses custom container images with the libraries that are required for the transformation logic preinstalled. The data pipeline reads the data from Cloud Storage and writes the data to BigQuery. You need to ensure cost-effective and secure communication between the pipeline and Google APIs and services. What should you do?

ALeave external IP addresses assigned to worker VMs while enforcing firewall rules.

BDisable external IP addresses and establish a Private Service Connect endpoint IP address.

CDisable external IP addresses from worker VMs and enable Private Google Access.

DEnable Cloud NAT to provide outbound internet connectivity while enforcing firewall rules.

Answer : C

Private Google Access allows VMs without external IP addresses to communicate with Google APIs and services over internal routes. This reduces the cost and increases the security of the data pipeline. Custom container images can be stored in Container Registry, which supports Private Google Access. Dataflow supports Private Google Access for both batch and streaming jobs.Reference:

Private Google Access overview

Using Private Google Access and Cloud NAT

Using custom containers with Dataflow

Google Cloud Certified Professional Data Engineer Exam Practice Test