The Up To The Immediate Present Guide To Professional-Data-Engineer Preparation Exams

NEW QUESTION 1

You use BigQuery as your centralized analytics platform. New data is loaded every day, and an ETL pipeline modifies the original data and prepares it for the final users. This ETL pipeline is regularly modified and can generate errors, but sometimes the errors are detected only after 2 weeks. You need to provide a method to recover from these errors, and your backups should be optimized for storage costs. How should you organize your data in BigQuery and store your backups?

• A. Organize your data in a single table, export, and compress and store the BigQuery data in Cloud Storage.
• B. Organize your data in separate tables for each month, and export, compress, and store the data in Cloud Storage.
• C. Organize your data in separate tables for each month, and duplicate your data on a separate dataset in BigQuery.
• D. Organize your data in separate tables for each month, and use snapshot decorators to restore the table to a time prior to the corruption.

Answer: D

NEW QUESTION 2

Suppose you have a dataset of images that are each labeled as to whether or not they contain a human face. To create a neural network that recognizes human faces in images using this labeled dataset, what approach would likely be the most effective?

• A. Use K-means Clustering to detect faces in the pixels.
• B. Use feature engineering to add features for eyes, noses, and mouths to the input data.
• C. Use deep learning by creating a neural network with multiple hidden layers to automatically detect features of faces.
• D. Build a neural network with an input layer of pixels, a hidden layer, and an output layer with two categories.

Answer: C

Explanation:
Traditional machine learning relies on shallow nets, composed of one input and one output layer, and at most one hidden layer in between. More than three layers (including input and output) qualifies as “deep” learning. So deep is a strictly defined, technical term that means more than one hidden layer.
In deep-learning networks, each layer of nodes trains on a distinct set of features based on the previous layer’s output. The further you advance into the neural net, the more complex the features your nodes can recognize, since they aggregate and recombine features from the
previous layer.
A neural network with only one hidden layer would be unable to automatically recognize high-level features of faces, such as eyes, because it wouldn't be able to "build" these features using previous hidden layers that detect low-level features, such as lines.
Feature engineering is difficult to perform on raw image data.
K- means Clustering is an unsupervised learning method used to categorize unlabeled data. Reference: https://deeplearning4j.org/neuralnet-overview

NEW QUESTION 3

You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:
The user profile: What the user likes and doesn’t like to eat
The user account information: Name, address, preferred meal times
The order information: When orders are made, from where, to whom
The database will be used to store all the transactional data of the product. You want to optimize the data schema. Which Google Cloud Platform product should you use?

• A. BigQuery
• B. Cloud SQL
• C. Cloud Bigtable
• D. Cloud Datastore

Answer: A

NEW QUESTION 4

Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data. They want to improve this performance while minimizing cost. What should they do?

• A. Redefine the schema by evenly distributing reads and writes across the row space of the table.
• B. The performance issue should be resolved over time as the site of the BigDate cluster is increased.
• C. Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.
• D. Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.

Answer: A

NEW QUESTION 5

You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics. Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded. The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

• A. Add capacity (memory and disk space) to the database server by the order of 200.
• B. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
• C. Normalize the master patient-record table into the patient table and the visits table, and create othernecessary tables to avoid self-join.
• D. Partition the table into smaller tables, with one for each clini
• E. Run queries against the smaller table pairs, and use unions for consolidated reports.

Answer: B

NEW QUESTION 6

You have Google Cloud Dataflow streaming pipeline running with a Google Cloud Pub/Sub subscription as the source. You need to make an update to the code that will make the new Cloud Dataflow pipeline incompatible with the current version. You do not want to lose any data when making this update. What should you do?

• A. Update the current pipeline and use the drain flag.
• B. Update the current pipeline and provide the transform mapping JSON object.
• C. Create a new pipeline that has the same Cloud Pub/Sub subscription and cancel the old pipeline.
• D. Create a new pipeline that has a new Cloud Pub/Sub subscription and cancel the old pipeline.

Answer: D

NEW QUESTION 7

You work for a mid-sized enterprise that needs to move its operational system transaction data from an on-premises database to GCP. The database is about 20 TB in size. Which database should you choose?

• A. Cloud SQL
• B. Cloud Bigtable
• C. Cloud Spanner
• D. Cloud Datastore

Answer: A

NEW QUESTION 8

You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
Decoupling producer from consumer
Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
Near real-time SQL query
Maintain at least 2 years of historical data, which will be queried with SQ
Which pipeline should you use to meet these requirements?

• A. Create an application that provides an AP
• B. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
• C. Create an application that writes to a Cloud SQL database to store the dat
• D. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
• E. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
• F. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.

Answer: A

NEW QUESTION 9

You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?

• A. Create an API using App Engine to receive and send messages to the applications
• B. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
• C. Create a table on Cloud SQL, and insert and delete rows with the job information
• D. Create a table on Cloud Spanner, and insert and delete rows with the job information

Answer: A

NEW QUESTION 10

Which of the following is not true about Dataflow pipelines?

• A. Pipelines are a set of operations
• B. Pipelines represent a data processing job
• C. Pipelines represent a directed graph of steps
• D. Pipelines can share data between instances

Answer: D

Explanation:
The data and transforms in a pipeline are unique to, and owned by, that pipeline. While your program can create multiple pipelines, pipelines cannot share data or transforms
Reference: https://cloud.google.com/dataflow/model/pipelines

NEW QUESTION 11

You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

• A. Increase the cluster size with more non-preemptible workers.
• B. Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.
• C. Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.
• D. Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.

Answer: D

Explanation:
Reference https://cloud.google.com/dataproc/docs/concepts/configuring-clusters/flex

NEW QUESTION 12

You want to use Google Stackdriver Logging to monitor Google BigQuery usage. You need an instant notification to be sent to your monitoring tool when new data is appended to a certain table using an insert job, but you do not want to receive notifications for other tables. What should you do?

• A. Make a call to the Stackdriver API to list all logs, and apply an advanced filter.
• B. In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.
• C. In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.
• D. Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.

Answer: B

NEW QUESTION 13

For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

• A. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
• B. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
• C. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
• D. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.

Answer: C

Explanation:
It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance,
If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.
It is recommended to avoid creating your Compute Engine instance in a different region from
your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.
Reference: https://cloud.google.com/bigtable/docs/creating-compute-instance

NEW QUESTION 14

Google Cloud Bigtable indexes a single value in each row. This value is called the .

• A. primary key
• B. unique key
• C. row key
• D. master key

Answer: C

Explanation:
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key.
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION 15

When you design a Google Cloud Bigtable schema it is recommended that you .

• A. Avoid schema designs that are based on NoSQL concepts
• B. Create schema designs that are based on a relational database design
• C. Avoid schema designs that require atomicity across rows
• D. Create schema designs that require atomicity across rows

Answer: C

Explanation:
All operations are atomic at the row level. For example, if you update two rows in a table, it's possible that one row will be updated successfully and the other update will fail. Avoid schema designs that require atomicity across rows.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

NEW QUESTION 16

You work on a regression problem in a natural language processing domain, and you have 100M labeled exmaples in your dataset. You have randomly shuffled your data and split your dataset into train and test samples (in a 90/10 ratio). After you trained the neural network and evaluated your model on a test set, you discover that the root-mean-squared error (RMSE) of your model is twice as high on the train set as on the test set. How should you improve the performance of your model?

• A. Increase the share of the test sample in the train-test split.
• B. Try to collect more data and increase the size of your dataset.
• C. Try out regularization techniques (e.g., dropout of batch normalization) to avoid overfitting.
• D. Increase the complexity of your model by, e.g., introducing an additional layer or increase sizing the size of vocabularies or n-grams used.

Answer: D

NEW QUESTION 17

What are all of the BigQuery operations that Google charges for?

• A. Storage, queries, and streaming inserts
• B. Storage, queries, and loading data from a file
• C. Storage, queries, and exporting data
• D. Queries and streaming inserts

Answer: A

Explanation:
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing

NEW QUESTION 18

Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set. You want to increase the AUC of the model. What should you do?

• A. Perform hyperparameter tuning
• B. Train a classifier with deep neural networks, because neural networks would always beat SVMs
• C. Deploy the model and measure the real-world AUC; it’s always higher because of generalization
• D. Scale predictions you get out of the model (tune a scaling factor as a hyperparameter) in order to get the highest AUC

Answer: D

NEW QUESTION 19

Cloud Bigtable is a recommended option for storing very large amounts of _____ ?

• A. multi-keyed data with very high latency
• B. multi-keyed data with very low latency
• C. single-keyed data with very low latency
• D. single-keyed data with very high latency

Answer: C

Explanation:
Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key. Cloud Bigtable is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at low latency, and it is an ideal data source for MapReduce operations.
Reference: https://cloud.google.com/bigtable/docs/overview

NEW QUESTION 20

You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?

• A. Create a dedicated service account, and use encryption at rest to reference your data stored in your Compute Engine cluster instances as part of your API service calls.
• B. Create encryption keys in Cloud Key Management Servic
• C. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
• D. Create encryption keys locall
• E. Upload your encryption keys to Cloud Key Management Servic
• F. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
• G. Create encryption keys in Cloud Key Management Servic
• H. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.

Answer: C

NEW QUESTION 21

Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

• A. Add ", UNNEST(person)" before the WHERE clause.
• B. Change "person" to "person.city".
• C. Change "person" to "city.person".
• D. Add ", UNNEST(city)" before the WHERE clause.

Answer: A

Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma. Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_resu

NEW QUESTION 22
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