2023 Provide Updated Google Professional-Data-Engineer Dumps as Practice Test and PDF
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Google Professional-Data-Engineer certification exam is a rigorous exam that requires a significant amount of preparation. Candidates must have extensive experience working with big data solutions and be familiar with the latest trends in data processing and analysis. Google Certified Professional Data Engineer Exam certification is highly valued in the industry and can lead to new career opportunities and higher salaries.
NEW QUESTION # 129
Your company is using WHILECARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
# Syntax error : Expected end of statement but got "-" at [4:11] SELECT age FROM bigquery-public-data.noaa_gsod.gsod WHERE age != 99 AND_TABLE_SUFFIX = `1929' ORDER BY age DESC Which table name will make the SQL statement work correctly?
- A. `bigquery-public-data.noaa_gsod.gsod`
- B. bigquery-public-data.noaa_gsod.gsod*
- C. `bigquery-public-data.noaa_gsod.gsod'*
- D. `bigquery-public-data.noaa_gsod.gsod*`
Answer: D
Explanation:
It follows the correct wildcard syntax of enclosing the table name in backticks and including the * wildcard character.
NEW QUESTION # 130
Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the cost of queries, your organization created a view called events, which queries only the last 14 days of dat
a. The view is described in legacy SQL. Next month, existing applications will be connecting to BigQuery to read the events data via an ODBC connection. You need to ensure the applications can connect. Which two actions should you take? (Choose two.)
- A. Create a new view over events_partitioned using standard SQL
- B. Create a new view over events using standard SQL
- C. Create a new partitioned table using a standard SQL query
- D. Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connection and shared "events"
- E. Create a service account for the ODBC connection to use for authentication
Answer: B,D
NEW QUESTION # 131
Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the dat
a. Which three machine learning applications can you use? (Choose three.)
- A. Supervised learning to determine which transactions are most likely to be fraudulent.
- B. Unsupervised learning to determine which transactions are most likely to be fraudulent.
- C. Supervised learning to predict the location of a transaction.
- D. Unsupervised learning to predict the location of a transaction.
- E. Reinforcement learning to predict the location of a transaction.
- F. Clustering to divide the transactions into N categories based on feature similarity.
Answer: B,C,F
NEW QUESTION # 132
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?
- A. Cloud SQL
- B. Cloud Datastore
- C. Cloud Bigtable
- D. Cloud Spanner
Answer: D
Explanation:
https://cloud.google.com/products/databases
NEW QUESTION # 133
Your company is in the process of migrating its on-premises data warehousing solutions to BigQuery. The existing data warehouse uses trigger-based change data capture (CDC) to apply updates from multiple transactional database sources on a daily basis. With BigQuery, your company hopes to improve its handling of CDC so that changes to the source systems are available to query in BigQuery in near-real time using log- based CDC streams, while also optimizing for the performance of applying changes to the data warehouse.
Which two steps should they take to ensure that changes are available in the BigQuery reporting table with minimal latency while reducing compute overhead? (Choose two.)
- A. Periodically use a DML MERGE to perform several DML INSERT, UPDATE, and DELETE operations at the same time on the reporting table.
- B. Periodically DELETE outdated records from the reporting table.
- C. Perform a DML INSERT, UPDATE, or DELETE to replicate each individual CDC record in real time directly on the reporting table.
- D. Insert each new CDC record and corresponding operation type to a staging table in real time.
- E. Insert each new CDC record and corresponding operation type in real time to the reporting table, and use a materialized view to expose only the newest version of each unique record.
Answer: C,D
NEW QUESTION # 134
Your company built a TensorFlow neural-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?
- A. Dropout Methods
- B. Dimensionality Reduction
- C. Serialization
- D. Threading
Answer: A
Explanation:
Explanation
Reference
https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505
NEW QUESTION # 135
Which of the following job types are supported by Cloud Dataproc (select 3 answers)?
- A. Pig
- B. Spark
- C. YARN
- D. Hive
Answer: A,B,D
Explanation:
Explanation
Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.
Reference: https://cloud.google.com/dataproc/docs/resources/faq#what_type_of_jobs_can_i_run
NEW QUESTION # 136
All Google Cloud Bigtable client requests go through a front-end server ______ they are sent to a Cloud Bigtable node.
- A. before
- B. once
- C. only if
- D. after
Answer: A
Explanation:
In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.
Reference: https://cloud.google.com/bigtable/docs/overview
NEW QUESTION # 137
You are building a model to make clothing recommendations. You know a user's fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available.
How should you use this data to train the model?
- A. Train on the existing data while using the new data as your test set.
- B. Continuously retrain the model on just the new data.
- C. Train on the new data while using the existing data as your test set.
- D. Continuously retrain the model on a combination of existing data and the new data.
Answer: D
NEW QUESTION # 138
You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of- Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about
100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required.
You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)
- A. HDFS with Hive
- B. MySQL
- C. MongoDB
- D. HBase
- E. Cassandra
- F. Redis
Answer: A,C,D
Explanation:
Explanation/Reference:
NEW QUESTION # 139
You are deploying 10,000 new Internet of Things devices to collect temperature data in your warehouses globally. You need to process, store and analyze these very large datasets in real time.
What should you do?
- A. Send the data to Google Cloud Datastore and then export to BigQuery.
- B. Send the data to Cloud Storage and then spin up an Apache Hadoop cluster as needed in Google Cloud Dataproc whenever analysis is required.
- C. Send the data to Google Cloud Pub/Sub, stream Cloud Pub/Sub to Google Cloud Dataflow, and store the data in Google BigQuery.
- D. Export logs in batch to Google Cloud Storage and then spin up a Google Cloud SQL instance, import the data from Cloud Storage, and run an analysis as needed.
Answer: C
Explanation:
Pubsub for realtime, Dataflow for pipeline, Bigquery for analytics.
NEW QUESTION # 140
You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query - -dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall dat
a. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?
- A. Create a separate table for each ID.
- B. Recreate the table with a partitioning column and clustering column.
- C. Use the LIMIT keyword to reduce the number of rows returned.
- D. Use the bq query - -maximum_bytes_billed flag to restrict the number of bytes billed.
Answer: B
NEW QUESTION # 141
You are working on a niche product in the image recognition domain. Your team has developed a model that is dominated by custom C++ TensorFlow ops your team has implemented. These ops are used inside your main training loop and are performing bulky matrix multiplications. It currently takes up to several days to train a model. You want to decrease this time significantly and keep the cost low by using an accelerator on Google Cloud. What should you do?
- A. Use Cloud TPUs without any additional adjustment to your code.
- B. Stay on CPUs, and increase the size of the cluster you're training your model on.
- C. Use Cloud TPUs after implementing GPU kernel support for your customs ops.
- D. Use Cloud GPUs after implementing GPU kernel support for your customs ops.
Answer: C
Explanation:
Cloud TPUs are not suited to the following workloads: [...] Neural network workloads that contain custom TensorFlow operations written in C++. Specifically, custom operations in the body of the main training loop are not suitable for TPUs.
NEW QUESTION # 142
To give a user read permission for only the first three columns of a table, which access control method would you use?
- A. Primitive role
- B. It's not possible to give access to only the first three columns of a table.
- C. Predefined role
- D. Authorized view
Answer: D
Explanation:
An authorized view allows you to share query results with particular users and groups without giving them read access to the underlying tables. Authorized views can only be created in a dataset that does not contain the tables queried by the view.
When you create an authorized view, you use the view's SQL query to restrict access to only the rows and columns you want the users to see.
Reference: https://cloud.google.com/bigquery/docs/views#authorized-views
NEW QUESTION # 143
Your company is in a highly regulated industry. One of your requirements is to ensure individual users
have access only to the minimum amount of information required to do their jobs. You want to enforce this
requirement with Google BigQuery. Which three approaches can you take? (Choose three.)
- A. Restrict BigQuery API access to approved users.
- B. Segregate data across multiple tables or databases.
- C. Disable writes to certain tables.
- D. Ensure that the data is encrypted at all times.
- E. Use Google Stackdriver Audit Logging to determine policy violations.
- F. Restrict access to tables by role.
Answer: A,E,F
NEW QUESTION # 144
Dataproc clusters contain many configuration files. To update these files, you will need to use the --properties option. The format for the option is: file_prefix:property=_____.
- A. value
- B. details
- C. id
- D. null
Answer: A
Explanation:
Explanation
To make updating files and properties easy, the --properties command uses a special format to specify the configuration file and the property and value within the file that should be updated. The formatting is as follows: file_prefix:property=value.
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-properties#formatting
NEW QUESTION # 145
An organization maintains a Google BigQuery dataset that contains tables with user-level dat A.
They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?
- A. Create and share a new dataset and view that provides the aggregate results.
- B. Create and share an authorized view that provides the aggregate results.
- C. Create and share a new dataset and table that contains the aggregate results.
- D. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.
Answer: D
NEW QUESTION # 146
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